Global progress against malaria has stagnated and novel medical interventions to prevent malaria are needed to fill gaps in existing tools and improve protection against infection and disease. Candidate selection for next-generation interventions should be supported by the best available evidence. Target product profiles and preferred product characteristics play a key role in setting selection criteria requirements and early endorsement by health authorities. While clinical evidence and expert opinion often inform product development decisions, integrating modelling evidence early and iteratively into this process provides an opportunity to link product characteristics with expected public health outcomes. Population models of malaria transmission can provide a better understanding of which, and at what magnitude, key intervention characteristics drive public health impact, and provide quantitative evidence to support selection of use-cases, transmission settings, and deployment strategies. We describe how modelling evidence can guide and accelerate development of new malaria vaccines, monoclonal antibodies, and chemoprevention. Nekkab, Malinga, Braunack-Mayer et al. discuss how modelling can be incorporated early on in the research and development of malaria tools alongside clinical evidence and expert opinion. In addition, population models can provide estimates of potential effectiveness of novel interventions to inform product criteria and support decision-making. Estimated malaria deaths in this region rose from 568,000 in 2019 to 625,000 in 2020 and declined marginally to 619,000 deaths in 2021. Moreover, the emergence of partial resistance to treatment with artemisinin in some parts of Africa threatens the efficacy of artemisinin-based combination therapies. Increasing insecticide resistance and invasion of the primary mosquito vector in India, Anopheles stephensi, into Africa may also lead to increasing transmission in urban areas, which could reverse progress made over recent decades3.There is an urgent need to accelerate development of novel malaria interventions to prevent infections and severe disease. Progress to reduce the impact of malaria has stalled worldwide alongside disruptions to services during the COVID-19 pandemic. Malaria cases in the African Region increased from 225 per 1000 population at-risk in 2019 to 234 in 2020 and remained similar at 229 cases per 1000 population in 20214. Furthermore, current malaria prevention measures do not reach all children. For example, seasonal malaria chemoprevention (SMC) is only recommended and deployed in highly seasonal settings with low drug resistance risk. Implementation of intermittent preventive treatment of malaria in pregnancy using sulfadoxine-pyrimethamine (IPTp-SP) is not recommended in low transmission or high SP resistance settings and there is limited guidance on the adverse consequences of drug–drug interactions5. Resource constraints, limited access to care, and low chemoprevention adherence in children and pregnant women, who are vulnerable to severe outcomes, continue to be major challenges. The development of novel interventions for malaria prevention must be accelerated to meet current and future needs. Incorporating all forms of evidence to guide decision-making early on during the product development pipeline will be vital in accelerating the process.While the 2021 World Health Organization’s (WHO) recommendation of the first malaria vaccine, RTS,S, was a major milestone, the vaccine took 30 years to develop, test, and pilot, and its availability, acceptance and use has not yet been demonstrated6. The Bill & Melinda Gates Foundation is developing intervention Target Product Profiles (iTPPs) to optimise investment in three next-generation medical interventions for prevention: novel immunological seasonal therapeutics using mAbs and long-acting injectable drugs, multi-seasonal interventions using second-generation malaria vaccines, and novel candidates or repurposed drugs for second-generation oral chemoprevention. In the case of malaria iTPPs for example, the documents define characteristics for development of novel intervention by how they are likely to be used and their indication, product components, target population and setting, safety, formulation, and pre-qualification date targets among other criteria for a base and upside case.Global funders, regulatory agencies, and researchers are expanding their pipelines for next-generation medical interventions for malaria prevention, guided by reference documents for candidate selection and investment decision-making in research and development (R&D). While guidance documents vary, they essentially outline the necessary characteristics required to support development decisions and optimisation of innovative products. The WHO developed Preferred Product Characteristics (PPCs), informed by technical working groups and public consultation, to guide and promote the development of various malaria prevention interventions such as vaccines, chemoprevention, and, recently, for monoclonal antibodies (mAbs) as well as vector control interventions. Medicines for Malaria Venture (MMV) also uses Target Candidate Profiles (TCPs) to support product development partnerships, pipeline development for long-acting injectable drugs, and repurposing, recombining, and developing antimalarials for SMCPrioritisation should be given to establishing a comprehensive evidence base to iteratively inform guidance documents and support selection of the most promising candidates, thus accelerating the development of novel medical interventions to prevent malaria. We argue that this evidence base should encompass the full range of quantitative and qualitative findings from clinical trials, modelling, and expert opinion. In the case of malaria prevention, selecting candidates that maximise the potential to achieve high public health impact and accelerate implementation requires decisions based on well-informed product criteria accounting for potential R&D bottlenecks, resources, and timeframes. Importantly, modelling evidence should be incorporated early in the drug development process, as modelling can uniquely link product criteria or properties to likely public health impact, which is not possible before or during clinical trials and is only possible after implementation. In addition to modelling existing interventions, modelling can estimate the potential range of impact on disease burden of a novel intervention to support building a value proposition document used to attract commercial development partners and engage with diverse stakeholders and partners that include those in malaria country programs and health agencies. Such modelling analyses also allow translation of clinical trial evidence to population impact across transmission settings, deployment strategies, or use-cases. Here, we focus our discussion on malaria prevention tools; however, this framework can also be expanded to malaria treatment, vector control, and to other diseases.7. While increased global investment has helped accelerate development in recent years, candidate selection guidance documents and priority use-cases are mainly informed by expert opinion and results of early stage clinical trials. Guidance documents have an important role to play in shortening R&D timelines and reducing costs, targeting priority use-cases and unmet needs, while ensuring selected candidates have a higher probability of demonstrating high impact for their given use-case. We posit that well informed decision-making along the product development pipeline, from discovery, proof-of-concept, and program implementation to impact, requires the iterative use of a comprehensive evidence-base that includes modelling evidence and allows for decisions to be adapted as new evidence becomes available  as well as the population-level benefit of deploying such a product to communities by quantifying the additional public health gains. Pharmaceutical approaches have traditionally prioritised efficacy outcomes at the individual level. For example, pharmacokinetics and pharmacodynamics (PK/PD) modelling support optimisation of drug availability and duration to improve drug efficacy for a particular disease target within the treated individual. In contrast, prevention targets for infectious diseases require considering both the individual-level benefit and an understanding of a product’s public health benefit, including changes to immunity acquisition by delaying infections or via any impact on population-level transmission dynamics. This is essential when we consider medical interventions that impact susceptibility to infection or aim to prevent disease progression to clinically relevant presentations or severe disease.8. While clinical evidence is essential to inform efficacy estimates and eventual registration and funding decisions for products, clinical evidence also informs model parameters, and modelling evidence in turn can improve clinical trial planning and design. For modelling evidence to be integrated within this process, it is essential for experts to be consulted early in the process to design research questions that modelling can address; an iterative loop between clinical trials, modelling, and expert opinion will reshape and transform criteria in guidance documents to support candidate selection.While clinical trials evaluate the individual-level benefit of products, they do not estimate the total public health benefit over extended time frames or capture any potential benefits of interrupting onward transmission with population-level interventions. Modelling captures these dynamics to link individual and community benefits, allowing clinical trial evidence to be integrated into updated models or public health estimates, and can also support translating clinical trial results into implementation considerations. In early product development stages, population-level modelling can provide quantitative evidence linking an intervention’s minimum key performance criteria, such as efficacy or duration with its projected public health impact and benefit to communities towards meeting health targets, thus contributing to a robust evidence baseDeveloping an accelerated and iterative process for the development of novel malaria prevention interventions should account for the following components.9. Addressing health inequity, in particular low access to health services and first line treatment for symptomatic children and low bed net distribution coverage is essential to bridging the gaps of malaria programs and elimination strategies. As social, financial, and epidemiological factors drive change over time, including inequity, unmet needs should be revisited in an iterative manner to anticipate future scenarios. Defining these unmet needs will allow for prioritisation and evaluation of supply requirements for various timeframes and public health goals, such as for elimination.The first step requires identifying who and where a malaria prevention intervention can meet the health needs of exposed populations. One approach is to consider the WHO-defined high burden high impact (HBHI) settings, ranking communities with mapped under-five mortality as a proxy for need. However, unmet needs can also be defined in communities without current prevention interventions, such as East Africa where SMC is not recommended or settings with perennial malaria transmission, or infants and pregnant women who are more vulnerable to severe outcomes and require higher consideration of drug safetyOnce unmet needs have been articulated, the use-case definition is an important driver to prioritise development of a new intervention and should be defined rigorously at each TCP, iTPP, or PPC update to ensure candidates are addressing unmet health needs. A use-case defines the target population(s) who will receive a new malaria prevention intervention , and where and when it should be deployed, ultimately influencing other criteria, including: product properties , logistic requirements , intervention characteristics , and deployment strategy . Considering these factors, modelling can evaluate how use-cases influence an intervention’s minimum criteria and vice versa.In the early stages, shaping novel malaria intervention characteristics in guidance documents should also be guided by community engagement through discussions between global funders, health agencies, researchers, evidence generators , product developers, local community leaders, and implementation specialists. These discussions should inform the identification of knowledge gaps, potential bottlenecks, and potential additional use-cases to refine the public health value proposition. Developing priority questions that inform modelling scenarios, such as comparing impact in seasonal and perennial settings or optimised number of rounds and timing of deployment, help guide evidence generation by informing clinical trial design and site selection targeting specific epidemiological settings, and the selection of model scenarios for predicting public health impact.Pre-clinical and early clinical evidence of first candidates provides initial profiling of feasibility, safety, and PK/PD relationships. This then informs where, and for which use-cases, a candidate can meet health needs, the expectations of regulatory approval, and implementation factors such as supply, delivery, and deployment strategies. In addition, early clinical evidence for these candidates sets threshold criteria for selection of future candidates that are expected to achieve non-inferiority, and informs model parameterisation to narrow down the exploration space between individual-level effects and population-level impact.Mathematical models of malaria transmission can guide thinking along the entire product development pipeline from clinical trial translation to use-case decisions for implementation. Modelling can initially explore the major drivers of an intervention’s impact across a broad spectrum of scenarios to identify what clinical evidence needs to be generated early. In addition, models can support the clinical evidence generation process by identifying minimum thresholds, linking intervention characteristics to health goals to inform guidance documents to the level of detail required for appropriate candidate selection by product developers. Modelling can provide evidence on where potential candidates would be best implemented to maximise impact and help drive policy and procurement decisions.Modelling can be integrated at each step in the development process to support articulating unmet needs, testing use-cases, translating clinical evidence, and setting achievable health goals to update guidance documents and adapt to new clinical evidence. As a result, evidence-based guidance and decision-making becomes an iterative process where modelling continuously supports the refinement of candidate profiles, rather than a linear process of development.The Swiss Tropical and Public Health Institute Disease Modelling group and the Bill & Melinda Gates Foundation have developed a collaborative framework that integrates the process described above to iteratively inform next-generation iTPPs for seasonal, multi-seasonal, and second-generation SMC interventions for malaria prevention. In June 2021, workshops were organised to identify challenges and priority questions and to launch the platform with a broad range of experts and stakeholders, including those based in malaria endemic regions and ensuring gender equity. These discussions focused on the importance of guidance documents during R&D, patient-centred development, community advocacy for target population acceptability, and, when to stop or to continue funding new interventions. Working groups identified the following priorities for modelling: (1) describing trade-offs between intervention and implementation factors; (2) translating clinical trial and modelling evidence to inform policy and investments; (3) informing clinical trial design and identification of standard of care (SOC) comparators; (4) accounting for financial resources and cost-of-goods; and (5) defining burden reduction criteria and timelines for achieving elimination.8. Modelling the mechanisms of individual-level factors and population-level transmission dynamics links predictions of public health burden reduction to key intervention characteristics. By predicting across an entire parameter space of intervention characteristics , modelling can quantify the importance and level of contribution of each characteristic to health outcomes and identify the minimum value at which intervention characteristics achieve defined health targets to directly inform iTPP criteria for different use-cases. Together with stakeholders from the 2021 convening, priority questions for modelling were ranked by most relevant scenarios to model at a first iteration for each given intervention by accounting for unmet health needs and priority use-cases. Clinical evidence, where and when available, informed parameter ranges to explore trade-offs and minimum criteria comprehensively. We give examples of this process in Table The collaborative framework generates modelling evidence using dynamic, individual-based malaria transmission models, such as the OpenMalaria model that was developed over a period of 15 years. Detailed models are coupled with additional analytical and statistical approaches to enable rapid and computationally efficient searches of multi-dimensional parameter spaces spanning a wide range of intervention characteristics and settingsModels provide supportive information for decision-making or informing candidate selection, funding, and target product profiles alongside other diverse sources, including clinical evidence and expert opinion. Although not the sole source informing selection criteria, models have a unique role to play before and during clinical trials and as clinical evidence is being acquired. While the additional role of modelling is clear, all models have limitations. To be informative for selection criteria, models must capture essential disease dynamics while remaining simple enough to run. Population model prediction uncertainty comes from model structure and parameter values. For example, assumptions about human behaviour patterns or intervention effect will impact predictions. Some use cases, such as pregnant women or travellers, are more challenging to model due to limited data. Discussions around a model’s limitations in generating evidence to inform TPPs is inherently part of the process itself, which requires stakeholders to formulate specific questions that are fit for purpose. The conversations around models, their limitations, and sources of uncertainty, and how to translate predictions to tangible evidence become the most valuable output.10. Unlike vaccines, where immune correlates of protection are challenging to define, mAbs offer the potential to provide early PK/PD evidence. While the first-generation candidate CIS43LS has demonstrated high and prolonged protection, sufficient PD evidence from dose de-escalation data are not yet available11. Modelling can address this knowledge gap by generating a comprehensive spectrum of PD characteristics from forthcoming early clinical trials to provide a broad range of potential impact predictions that can be refined iteratively as more data is generated.Population-level transmission modelling can translate clinical efficacy outcomes to population-level effectiveness by integrating pharmacological evidence informing individual-level protection. Take, for example, monoclonal antibodies for infection prevention in seasonal settings. Currently very little is known about the PK/PD relationship of monoclonal antibodies. Previous modelling has shown that the protection of an anti-infective malaria monoclonal over time, informed by PK/PD data and models, is an important driver of public health impact13.For malaria prevention, imperfect tools, such as perennial malaria chemoprevention (PMC), SMC, and RTS,S vaccination, have been deployed in moderate to high transmission settings. The public health and cost-effectiveness targets for novel interventions can be informed by the standard-of-care (SOC) comparators when the use-case is well defined and alternative interventions already exist. In the absence of a comparator, ranges of desired health targets can be iteratively assessed, but comparison of effectiveness to existing prevention technologies is critical to refine the value proposition for further R&D investment. Without population-based modelling before Phase 3 clinical trials, it is challenging to evaluate the effectiveness of candidates with early clinical data alone. Modelling can quantify effectiveness for different intervention characteristics, use-cases, and evaluation periods, as well as easily translate different endpoints to guide policy decisions. For example, the public health impact and cost-effectiveness of the RTS,S malaria vaccine was evaluated by four independent modelling groups using different mathematical models of malaria transmission. This body of work estimated disability-adjusted life years (DALYs) for both a three-dose and four-dose vaccine schedule more than four years prior to the start of the pilot studies. These cost-effectiveness estimates demonstrated that a partially efficacious vaccine could have significant impact at the population level, critical for the WHO’s recent recommendation of RTS,S14. Currently, the efficacy profiles of first-generation anti-infective vaccines like RTS,S and, potentially, R21 support their use in seasonal settings. Yet, many questions remain regarding implementation factors, combinations of the vaccine with oral chemoprevention programs, and impact across different epidemiological settings. Modelling can support this by predicting the likely impact of such factors prior to expensive clinical trials and pilot studies and explore the feasibility of these tools to achieve future elimination targets.Guidance documents for mAbs, next-generation chemoprevention, and vaccines in development currently prioritise preventing clinical illness and severe disease in vulnerable populations, including infants, children under five, and pregnant women. When use-cases are uncertain, modelling can simulate scenarios for different age groups, seasonal profiles, and transmission intensities to demonstrate where interventions will be most impactful and reassess when new evidence is onboarded. For example, WHO recommends malaria vaccines for children aged five to 17 months old in perennial settings with potential for seasonal application in children under five years old. However, these target ages and seasonal use-case only came to light as clinical evidence was accrued because the vaccine prevented infections for a shorter duration than hoped during the Phase 3 studiesThe malaria vaccine example described here provides a lesson to heed for novel malaria intervention development. Firstly, use-cases of novel interventions should be re-evaluated with an improved understanding of the limits of an intervention’s efficacy and duration. Thus, understanding the efficacy and duration of novel malaria interventions as early as possible will help not only to select or reject candidates and optimise duration properties, but also assess and reassess appropriate use-cases iteratively. Modelling supports this by demonstrating the trade-offs between deployment factors and intervention characteristics to identify minimum criteria well before Phase 3 trials. In some cases it will provide efficacy and duration cut-offs (clear ‘no-go’ criteria) for candidate use in different uses-cases. However, if early clinical evidence suggests candidates will struggle to ever meet these initial efficacy and duration requirements defined by iTPPs use-cases, the community should not always discard classes of candidates or novel interventions, but critically and discriminately revaluate if there is value of this efficacy and duration profile for another use-case for malaria prevention.With increasing threats of malaria drug and insecticide resistance, there is now a need for novel malaria prevention interventions to improve public health impact. As the malaria community moves forward to invest in next-generation interventions, guidance documents are crucial to ensure that the best evidence supports the criteria for candidate section and decision-making for implementation. While clinical evidence and expert opinion will initially play an essential role in informing these criteria, mathematical modelling can accelerate this process and provide robust evidence of candidate characteristics and deployment strategies that are likely to lead to a higher public health impact for different use-cases and enhance the value proposition for a given development candidate. Modelling should be incorporated early in the evidence generation process, to improve the translation of clinical trial efficacy estimates and to support use-case and implementation strategy decisions. Public health impact predictions from modelling studies that include detailed intervention dynamics are currently underutilised for setting selection criteria early in development. Thus, including such analytic tools early on provides a unique opportunity to accelerate the development of malaria interventions for optimised use-cases and deployment strategies.


The provision of quality science education is a global priority beset by longstanding challenges, which can be amplified in rural and regional contexts. This creates a dual problem where stakeholders must focus on the improvement of science education outcomes whilst being cognisant of the established divided between metropolitan and non-metropolitan learners. Considering the recent positive TIMSS results which showed equitable science results for regional, remote and metropolitan Australian year 4 students, this paper aims to examine the relationship between primary teachers’ school location and their science teaching efficacy beliefs and reported science teaching practices. A total of 206 Australian primary science educators responded to a cross sectional quantitative survey. Descriptive statistics, analysis of variance (ANOVA) and chi-square analyses found no statistically significant differences between metropolitan and non-metropolitan teachers on measures of science teaching efficacy beliefs and reported science teaching approaches. This apparent contradiction of established research themes merits deeper school and student-focused research to understand the practical implications that could arise from these findings. Many primary aged learners are limited in their capacity to apply the knowledge and skills gained in their formal science education experiences in ways that make meaning beyond the classroom. Evidence from the Trends in International Mathematics and Science Study (TIMSS) provides robust, if fallible, evidence that most year 4 students across the majority of participating nations have difficulty in generalising their science learning to new contexts ?It is possible that the recent evidence of more equitable primary science performance between metropolitan and non-metropolitan learners  have been consistently employed as measures of inservice and preservice teachers’ STEBs for over three decades  = 1.845, p = 0.418, meaning that the sampled schools did not vary considerably from the target population in terms of parental occupation, parental education, school remoteness and Indigenous student enrolment percentages.There were 173 schools (> 10%) from the target population represented in this project. The Australian Curriculum and Reporting Authority ACARA,  designatA series of recruitment approaches were employed as part of a non-probabilistic, purposive sampling of the target population of primary teachers. Two email invitations were sent to each school in the final two semesters of the 2021 school year. Physical mailouts, with QR codes to access the online survey, were sent to each school between email invitations. These primary recruitment strategies were supplemented by opportunistic snowball sampling and sharing across online platforms, both professional and social.The final sample of 206 primary teachers, representing 0.67% of the target population, was strong in statistical terms  beliefs and Science Teaching Outcome Expectancies (STOE) scales  and redundant (e.g. ‘Project-based learning’) responses. Open responses deemed appropriate by the research team included resource suites (e.g. ‘Inquisitive’), science fairs and integrated STEM approaches. Each participant was assigned a metric ‘Total Approaches’ score based on the number of relevant approaches selected or otherwise identified in their response.Respondents were asked to identify all of the areas of the science curriculum they had addressed in their teaching during the past year. Eleven dichotomous items covered the strands and sub strands of the current Australian K-10 Science Curriculum ACARA, . Table 3Initially, descriptive statistics were calculated for the measures of science teacher efficacy (i.e. PSTE and STOE) and reported science teaching approaches  for the groups of metropolitan or non-metropolitan-based teachers. To determine the difference between the two groups, a one-way ANOVA was computed on the PSTE, STOE, Total Approaches and Curriculum Coverage variables Pallant, . For addThere are very few observable differences between the metropolitan and non-metropolitan groups on the PSTE, STOE, Total Approaches and Curriculum Coverage measures. Table g = 0.194) indicating a small advantage to the metropolitan teachers on the PSTE scale, the difference was not significant (p = 0.162). Additionally, there were no statistically significant between-group differences on the STOE subscale (p = 0.720), the Total Approaches reported (p = 0.791) and the Curriculum Coverage scores (p = 0.992).There were no statistically significant differences detected between the metropolitan and non-metropolitan groups on the PSTE, STOE, Total Approaches and Curriculum Coverage measures. Table X2  = 5.518, p = 0.019, whereas their metropolitan counterparts reported using Debate strategies with comparatively greater frequency, X2  = 4.765, p = 0.029. However, the significance of these findings in practice is contestable due to the lower overall frequency of use for Peer Tutoring and Debate approaches.More precise chi-square analyses were conducted on participants’ responses to the 38 specific framework approaches, and the number of ‘other’ approaches identified to account for the lack of sensitivity inherent in the broad Total Approaches measure. Table The findings presented in this paper seem to align with the most recent Australian TIMSS results, which showed no statistically significant differences in metropolitan, regional and rural year 4 students’ science achievement  is widely adopted, it cannot cater for issues such as for individual movement between jurisdictions and requires categorisation that may not have any tangible meaning in practice, thus creating an artificial sense of accuracy. Indeed, many educators are likely to have completed their ITE in metropolitan areas and thus may have rural and regional experiences and perspectives that differ from those with more long-term connections to their communities. The importance of the metropolitan- and non-metropolitan-focused analyses presented in this paper should not be overstated as place inherently intersects with factors such as socio-economic status and gender, in ways that were not considered. Additionally, the absence of students as a data source prevents a clear link between the educators’ STEBs and teaching practices, and student outcomes from being established without relying heavily on the theoretical framework (Efficacy). Also, despite the rigour of the framework of approaches (Appendix 1) underpinning this paper, it can neither be a complete reflection of all the approaches that may comprise a primary science teachers’ professional and pedagogical experience repertoire (Loughran et al., The findings presented in this paper contradict much of the existing literature that has described educational divides between metropolitan and non-metropolitan learners. When considered alongside the outlying equitable TIMSS science achievement for Australian year 4 students across regional, rural and metropolitan centres (Thomson et al.,


Autoimmune blistering diseases (AIBDs) comprise a group of rare conditions marked by autoantibodies that specifically target intercellular adhesion molecules. Despite the progress made in comprehending the disease and the increasing number of treatment options available, there is still no definitive cure for AIBDs such as pemphigus, and it continues to have a devastating impact on those affected. The challenges in achieving new approved therapies for AIBDs are complex and multifaceted. One significant obstacle was the prior lack of validated and standardized outcome measures, which are crucial for ensuring precise comparisons between new and traditional therapies. This gap in knowledge has prompted the development of minimal clinically important differences (MCIDs), which enable efficient and reliable comparison of therapeutic outcomes between trials. MCID is defined as the minimum difference in an outcome measure that indicates a clinically significant improvement/deterioration in disease severity. Additionally, MCIDs provide a patient-centered approach to evaluating treatment efficacy, by considering whether patients experience a subjective improvement in their symptoms. Therefore, this literature review will examine the derivation and significance of MCIDs for various scoring systems in AIBDs. The purpose is to understand the various methods which have been used to calculate MCIDs in AIBD and to recommend strategies to improve the reliability of MCID calculations.2Autoimmune blistering diseases (AIBDs) encompass a group of rare conditions that result in the formation of blisters on the skin and mucous membranes, with examples including pemphigus and pemphigoid. The severity of AIBDs can be attributed to the fact that the rupturing of its blisters creates an opening for infections to opportunistically invade, putting the patient at increased risk of sepsis and ultimately death. This substantial infection risk is underscored by the high mortality rate of pemphigus, a type of AIBD, amounting to 1.7 to 3 times that of the general population . This in3In medicine, the ability to accurately assess disease severity is often hindered by the lack of consensus on the best scoring systems to carry out such an assessment, which can lead to inaccurate assessments of patient treatment outcome and efficacy. This is especially true for AIBDs such as pemphigus before the development of its current scoring systems, as a systematic review conducted in 2006 highlighted the lack of universally recognized outcome measures as the contributing factor to the shortage of high-quality clinical trials assessing treatments for pemphigus . As a reAlthough assessment tools are valuable in dermatology, it is important to recognize that most of these clinician-reported outcome measures are designed to detect only small differences in severity. However, such minimal differences may not be significant enough to impact patients’ quality of life or measurable disease burden. Therefore, the concept of the MCID was created, which refers to the smallest difference in an outcome measure that reflects a clinically significant improvement in disease severity . Values 3.1There are various different methods of calculating MCID which are summarized below in Once the anchor’s threshold of clinical significance is established, the MCID is then calculated as the smallest change in the scoring system that corresponds to a meaningful change in clinical improvement indicated by the anchor .4The following section will explore the variations of methods that previous studies have employed to calculate MCIDs of AIBD.4.1The PDAI is a scoring system developed by the International Pemphigus Definitions Committee over a period of three years after 2006, to evaluate the extent of pemphigus disease with a potential score ranging from 0-263 . It asseTo date, there have been no published studies on calculating the MCIDs for PDAI. The absence of this knowledge is likely because calculating MCID requires data on longitudinal changes in clinical scores in response to treatment from a reasonable sample size, which is difficult to collect and analyze for a disease as rare and severe as pemphigus . Determi4.2The ABSIS is a scoring system that was initially developed for evaluating pemphigus but has since been widely used for all autoimmune blistering diseases following its introduction in 2007 , 15. TheThere has been one study that calculated MCIDs for ABSIS led by Wijayanti et al. in 2017 (n=27) for bullous pemphigoid . The stu4.3The Epidermolysis Bullosa Disease Activity and Scarring Index (EBDASI) is a scoring system that evaluates epidermolysis bullosa disease activity and damage separately in each of its five sections: skin, scalp, mucosa, nails, and other epithelialized surfaces. The skin section is the most comprehensive and includes 12 anatomical sites. The algorithm assigns a combined aggregate score of 506, with 276 for total activity and 230 for damage . The EBDThere has been one study led by Jain et al. in 2016 (n=29) that likewise used the anchor-based method and ROC analyses to calculate MCIDs for EBDASI by anchoring against the 15-point Likert scale of change . A crite4.4The Bullous Pemphigoid Disease Area Index (BPDAI) is a scoring system developed in 2007 for bullous pemphigoid and is scored from 0-360 based on four components: body surface area percentage involved, peak pruritus numerical rating scale (NRS) score, disease extent index, and area index . The pruIn Wijayanti et al.’s same study in 2017 which also calculated MCIDs for ABSIS, Wijayanti et al. concluded that a 4-point decrease in BPDAI scores indicated clinical improvement (MCID=4), whereas a 3-point decrease indicated clinical deterioration (MCID=3) . In addi4.5The investigator global assessment (IGA) for pemphigus is a new scoring system that is still in the process of being developed and validated by a previous study as of this writing.The IGA is scored separately from 0-4  for mucosal and cutaneous lesions, making it more simplistic than PDAI and ABSIS scores. The U.S. Food and Drug Administration (FDA) prefers to use the IGA to assess the primary endpoint of clinical drug trials over the PDAI and ABSIS because the IGA provides a more simplistic and clinically meaningful ordinal score that patients can easily understand . Develop4.6The variation in methods which have been used to calculate MCIDs highlighted above underscores the inconsistency of calculating MCIDs of scoring systems for AIBD. This appears to be a common theme for many other scoring systems in dermatology . The fol4.7The inconsistency in calculating MCIDs is evident from the various methods used, as highlighted in the discussion so far. The absence of international guidelines for calculating MCIDs leads to varying sample sizes in studies, which are often on the low end for rarer and more severe diseases like pemphigus. This has negative consequences on the generalizability of MCIDs. It is known that the MCID may exhibit variability based on differences in population and context, and to address this issue, it is imperative to accept that MCIDs can and should be re-calculated should the opportunity arise for sample sizes to be expanded , 26.patient’s perspective, it appears to be logical that anchoring to a score that patients give themselves, such as QOL scores, would be more favorable than a physician’s score such as the 15-point Likert scale of change. However, in as much as 41.4% of MCID calculations in dermatology, the anchor was rated by the physician, though there are advantages and disadvantages to both (In addition, from the examples above, it is evident that MCIDs vary with the choice of anchor. Given that MCIDs reflect the level of improvement from the  to both . One adv to both . These f4.8Overall, the inconsistency of MCID calculations identified arises from the use of various methods and anchors in different studies. The absence of standardized guidelines leads to varying sample sizes, particularly in rare and severe diseases, which affects the generalizability of MCIDs.To address this issue, it is crucial to recognize that MCIDs can and should be recalculated when opportunities arise to expand sample sizes. To improve the credibility of MCID calculations in dermatology, it is recommended to establish international guidelines that provide standardized methods for calculating MCIDs. These guidelines should address issues such as sample size determination and the selection of appropriate anchors based on the specific disease, its severity, and objectives of the studies. The selection of appropriate anchors for MCID calculations should not only consider the specific disease and its severity but also take into account the objectives of the studies. Different objectives may require different types of anchors to be used in calculating MCIDs. For instance, studies aiming to assess the impact of treatments on disease severity may use patient-reported anchors, whereas studies focusing on clinical significance may opt for physician-reported anchors. By acknowledging the influence of study objectives on anchor selection, researchers can ensure that MCID calculations align more closely with the intended applications of the study results. Furthermore, collaboration among researchers and clinicians can facilitate the pooling of data and resources to achieve larger sample sizes, enhancing the accuracy and generalizability of MCIDs. Overall, by promoting consistency and transparency in the calculation of MCIDs, the reliability and usefulness of these values in dermatology research and clinical practice can be enhanced.5The current inconsistencies in MCID calculations underscore a significant gap in literature in this area. The determination and application of MCIDs in AIBD are essential for evaluating treatment effectiveness and enhancing patient care. By calculating MCIDs through anchor-based and distribution-based approaches, clinicians can gain insights into clinically significant improvements and make informed treatment decisions. Further research is needed to establish MCIDs for different scoring systems using both patient- and physician-reported anchors and enhancing sample sizes to improve the application of MCID calculations in dermatology.HT wrote the original manuscript. CS and DFM have made a substantial contribution revising the manuscript critically related to the accuracy or integrity of any part of the work. All authors contributed to the article and approved the submitted version.


It is not known how large language models, such as ChatGPT, can be applied toward the assessment of the efficacy of medications, including in the prevention of migraine, and how it might support those claims with existing medical evidence.We queried ChatGPT-3.5 on the efficacy of 47 medications for the prevention of migraine and then asked it to give citations in support of its assessment. ChatGPT’s evaluations were then compared to their FDA approval status for this indication as well as the American Academy of Neurology 2012 evidence-based guidelines for the prevention of migraine. The citations ChatGPT generated for these evaluations were then assessed to see if they were real papers and if they were relevant to the query.ChatGPT affirmed that the 14 medications that have either received FDA approval for prevention of migraine or AAN Grade A/B evidence were effective for migraine. Its assessments of the other 33 medications were unreliable including suggesting possible efficacy for four medications that have never been used for the prevention of migraine. Critically, only 33/115 (29%) of the papers ChatGPT cited were real, while 76/115 (66%) were “hallucinated” not real papers and 6/115 (5%) shared the names of real papers but had not real citations.While ChatGPT produced tailored answers on the efficacy of the queried medications, the results were unreliable and inaccurate because of the overwhelming volume of “hallucinated” articles it generated and cited. Artificial intelligence (AI) models are poised to synthesize useable outputs, from an ever-expanding medical literature, that physicians and patients can use to inform clinical decisions. For example, an AI model trained on electrocardiograms of migraine patients, with and without aura, was used to suggest that migraine with aura is an independent risk factor for atrial fibrillation . A compuThis project was exempted from Institutional Review Board Review as it did not meet the criteria for Stanford University’s definition of Human Subject research requiring IRB approval per OHRP 45 CFR 46.102.We chose the medications to be assessed by aggregating the list of Food and Drug Administration (FDA)-approved medications for the prevention of both episodic and chronic migraine, with the medications addressed in the American Academy of Neurology (AAN) 2012 evidence-based guidelines for the prevention of migraine . We elecnegative recommendations in the 2012 AAN guidelines, and medications with no role in the prevention of migraine as described above.These 47 medications were assigned to one of six categories: medications that are FDA-approved for the prevention of migraine, medications that are not FDA-approved for the prevention of migraine but have Grade A or B evidence in the 2012 AAN guidelines, medications that are not FDA-approved for the prevention of migraine and have Grade C evidence in the 2012 AAN guidelines, medications that are not FDA-approved for the prevention of migraine and have Grade U evidence in the 2012 AAN guidelines, medications that are not FDA-approved for the prevention of migraine and have Grade A, B, or C OpenAI’s ChatGPT-3.5 was utilized with a new chat session and previous chat history cleared. Each of the 47 medications were queried by one author (LSM) into ChatGPT in one session over 2 days on April 14, 2023 and April 16, 2023 using the phrasing, “is [medication] an effective medication for the prevention of migraine? Give 3 citations if they exist.” ChatGPT’s responses on the efficacy were then manually sorted into five categories, “Yes,” “Some evidence it is effective,” “Limited evidence or research,” “No evidence,” and “Would not give opinion.”The up to three citations that ChatGPT would give in support of its assessment were then documented and the titles searched on both Google Scholar and PubMed to determine if they were real literature or “hallucinations.” Real papers were then assessed for relevance to the query. These literature search engines were then also surveyed for the first three papers that would result for the search “[medication] migraine prevention.” ChatGPT citations of real papers for each of the medications were then compared to the top three results from the searches of each of Google Scholar and PubMed.This methodology was determined prior to the initiation of any queries of medications into ChatGPT. This is the primary analysis of these data. There were no missing data as ChatGPT rendered a decision on all 47 medications inputted.We then assessed ChatGPT’s ability to evaluate the literature through an area under the receiver operating characteristic (ROC) curve, including 95% confidence intervals, for both all medications and the subset of oral medications only. For this, medications in categories 1, 2, and 3 were given the binary outcome “1” denoting their recommendation for the prevention of migraine. Medications in categories 4, 5, and 6 were given the outcome “0” denoting they were not recommended for the prevention of migraine. ChatGPT’s recommendations were then assigned relative strengths of recommendation on a scale of 0 to 1. Answers mapped to “Yes” were assigned “1”; answers mapped to “some evidence it is effective” were assigned “0.66”; “Limited evidence or research as “0.33” and “No evidence” or “would not give opinion” were assigned “0.”Descriptive statistics were calculated within Microsoft Excel including percentages. Area under the curve for the ROC curve was calculated in STATA, Version 14 (StataCorp LLC).The full transcript of the conversations with ChatGPT used to generate the data is presented in ChatGPT correctly affirmed the efficacy of medications that were either FDA-approved for the prevention of migraine or graded as “Established as effective” or “Probably effective” by the AAN; it deemed all nine FDA-approved medications and all five preventives with Grade A or B evidence effective . HoweverChatGPT also did not reliably assess medications that have received negative recommendations from the AAN. Of the seven medications the AAN has recommended against with either Grade A, B, or C evidence, 3/7 were reported as having some evidence of efficacy and the other four were reported as having limited evidence or research. Of the five medications that have never been assessed for migraine prevention, only one, acarbose, was correctly stated to have no evidence of its use.To assess the overall utility of the ChatGPT model for assessment of the evidence, we determined the area under the curve (AUC) for the ROC curve for all medications  and for Concerningly, ChatGPT then predominantly supported its assessments with “hallucinated” papers. Examples of fabricated citations and summaries are presented in The propensity for ChatGPT to support its assessment with “hallucinated” papers was investigated further. Seventeen of the 41 medications (41%) for which ChatGPT produced citations were supported by three not real references. Of those with at least one real citation, 9/41 (22%) had one real citation and 10/41 (24%) had two. Only 5/41 (12%) were supported by three real studies. That said, of these 5 medications for which ChatGPT reported three real studies, only two of them, galcanezumab and onabotulinumtoxinA, were supported by three real and relevant studies.To assess for the relative utility of ChatGPT compared to current literature review standards with the use of Google Scholar and PubMed, we sought to compare the citations generated by ChatGPT to the top three results for a comparable search on those platforms for the medications . Of the While previous studies and popular media have lauded ChatGPT as a next step in medical information and diagnosis, our study suggests that it is only capable of affirming the efficacy of the medications most widely known and supported for the prevention of migraine and otherwise gave unreliable answers on efficacy of medications; the AUC for both medications overall and for the oral medications overall is not to a sufficient level to be useable when false assessments of the literature could lead to worse outcomes for patients.Critically, its propensity to “hallucinate” papers to support its reports is concerning, and a known problem with the model . Only 34The worrisome nature of the hallucinations is not only due to them being not real, but also due to the highly plausible sounding nature of the citations and summaries, and the use of real names of researchers in the field, such that readers may easily mistake them for real papers. The hallucinated citations are convincing and require thorough literature checks to ensure they are not real. This speaks to the advanced nature of the ChatGPT natural language processing as well as the model’s confidence. ChatGPT never expressed any concern about these being not real citations and so readers, potentially including clinicians and patients, would have no immediate reason to doubt their validity. It is not unimaginable that patients may present to clinic with requests for irrelevant medications to prevent migraine based on conversations with ChatGPT which included “hallucinated” articles.Headache alone and indeed Headache’s current editor-in-chief is cited as first author on two of the not real papers. Journals and clinicians may find themselves in difficult positions with inquiries on credible-sounding but non-existent papers attributed to them or their journals. Furthermore, in many of the citations, ChatGPT generated PubMed identifiers (PMID) which are already in use by other real articles. For example, ChatGPT assigned the fictious paper “Chronic Migraine: Patient-Reported Outcomes From the Phase 3b LIBERTY Study” the PMID 32887703, however this PMID is already in use by the 2020 article “COVID-19 and Children With Diabetes-Updates, Unknowns, and Next Steps: First, Do No Extrapolation” (Journal reviewers and editors, as well as, researchers should be concerned too, as ChatGPT quickly attributed these not real papers to real journals and real authors, similar to previous reports in traditional journalism . In our In addition, our interest in comparing the ChatGPT generated citations to those of Google Scholar and PubMed was not realistic because we did not foresee the extent of ChatGPT’s hallucinated articles. Comparatively, searches on Google Scholar and PubMed yielded AAN guidelines and high-yield review papers that could answer the readers’ inquiry, albeit with more effort and provided the reader had access to the paper , 9, 10.Our study is limited by reproducibility. Interactions with ChatGPT are almost by definition, unique and readers are unlikely to get these exact results when attempting to replicate this. Indeed, as ChatGPT is a dynamic and evolving system, as time goes on and additional interactions and information is added to it, the outputs too will change. While the exact results are likely not reproducible, we have included the full transcript of our interaction with ChatGPT to generate our data set for transparency . We alsoThe role of artificial intelligence in the practice of medicine is rapidly evolving and determining the best usage of these tools is ongoing. While ChatGPT produced tailored answers on the efficacy of the queried medications, the results were unreliable and inaccurate because of the overwhelming volume of “hallucinated” articles it generated and cited. Well-researched and verified medical websites such as UpToDate, WebMD, or MedScape, in addition to traditional online literature search engines including Google Scholar and PubMed should remain as the primary resources for clinicians to determine the optimal treatments for their patients and for patients to learn more about their illness and therapeutic options.The original contributions presented in the study are included in the article/LM and NZ: study concept and design, drafting of the manuscript, revising it for intellectual content, and final approval of the completed manuscript. LM: acquisition of data and analysis and interpretation of data. All authors contributed to the article and approved the submitted version.Funding for the open access publication fee was provided by Stanford University’s School of Medicine, Department of Neurology.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


International reverberations are bound to follow what remedial actions may be taken in the US, the origin of current world regulatory procedures to control alleged cancer causing agents.The 1958 Delaney amendment to the Federal Food Drug and Cosmetics Act prohibited food additives causing cancer in animals by appropriate tests. Regulators responded by adopting chronic lifetime cancer tests in rodents, soon challenged as inappropriate, for they led to very inconsistent results depending on the subjective choice of animals, test design and conduct, and interpretive assumptions. Presently, decades of discussions and trials have come to conclude it is impossible to translate chronic animal data into verifiable prospects of cancer hazards and risks in humans. Such conclusion poses an existential crisis for official agencies in the US and abroad, which for some 65 years have used animal tests to justify massive regulations of alleged human cancer hazards, with aggregated costs of $trillions and without provable evidence of public health advantages. This article addresses suitable remedies for the US and potentially worldwide, by critically exploring the practices of regulatory agencies vis-á-vis essential criteria for validating scientific evidence. According to this analysis, regulations of alleged cancer hazards and risks have been and continue to be structured around arbitrary default assumptions at odds with basic scientific and legal tests of reliable evidence. Such practices raise a manifold ethical predicament for being incompatible with basic premises of the US Constitution, and with the ensuing public expectations of testable truth and transparency from government agencies. Potential remedies in the US include amendments to the US Administrative Procedures Act, preferably requiring agencies to justify regulations compliant with the Daubert opinion of the Daubert ruling of the US Supreme Court, which codifies the criteria defining reliable scientific evidence ENDORSERS of this paperHermann M. Bolt1, Wolfgang Dekant2, José Luis Domingo3, Jay I. Goodman4, Angelo Moretto5, Emanuela Testai6, Nico P.E. Vermeulen7, Kendall B. Wallace8“We the People of the United States” constituted the US government to serve their quest for freedom and the pursuit of happiness.Still, motivated by natural instincts to prevail, official regulatory agencies have continually labored to achieve autonomous authority, obtained in the US by statutory authorization to emit regulations with the power of law, and in most other countries by deferential legislative approval of agency protocols. On these grounds, regulatory agencies in the US and around the world set out to police the ambitions of special interests and to modify choices and behaviors of free citizens.Such broad agendas include health, safety and environmental regulations to prevent cancer risks in humans. Such regulations require evaluations of potential human cancer hazards from natural and artificial molecules ubiquitous in the environment: a set of substances which delimits the scope of the current paper. At prevailing low exposures, these agents offer no discernible evidence of adverse effects regarding cancer risk. Overall, regulations would not be warranted if such natural low exposures are not exceeded, but could become of interest when duration and intensity of exposure may exceed thresholds of no-observable adverse effects. Hence, the science of toxicology and scores of professionals eager to sustain or bar policies and regulations, laboring for transparent, truthful, testable, and measurable scientific evidence about hazards and thresholds of interest.2What are the standards of evidence appropriate for official regulation? During the last decades, much has been written recommending the use of evidence in public life. The US National Library of Medicine carries over a half million publications on evidence-based medicine, toxicology, epidemiology and a host of other disciplines. No extant paper, however, has attempted to highlight the essential criteria defining what is truthful, reliable, and thus functional scientific evidence, ready for effective technologies and sensible public and personal policies.Here, toxicology being an experimental natural science, truthful evidence needs qualification. Unlike the absolutes of purely cerebral disciplines such as mathematics, geometry or formal logic, all experimental natural sciences postulate empirical truths in a probabilistic context, due to the inherent approximations of observations and measurements, the multitude of potentially confounding variables and the natural vagaries of atoms, molecules, radiations and overall matter.A research activity dealing with knowledge-in-the-making and dedicated to test hypotheses and theories about the mechanics of the physical world.A body of tested and self-standing knowledge ready for technology and policy applications, and to inspire new testable hypotheses and theories.Although nominally provisional, such truths—or natural laws of experimental science—are confirmed by the operational success of countless technological applications: wholesome foods and safe water are the norm, innumerable chemicals are safely used, personal and occupational hygiene keep us healthy, medicines and vaccines cure, and so on. Undeniably, the factual reliability of technological applications vouches for their foundation on true scientific evidence: a context where toxicology is also expected to warrant true scientific evidence in justifying public policies and regulations. Such evidence, as common to all natural sciences, is developed, tested and approved in two separate phases:,4Research hypotheses are considered scientific in the context of prospecting for and motivating scientific research, but are not part of validated scientific knowledge. Venture capital may support testing of research hypotheses hoping for the big win with no guarantee of success, but no sensible entrepreneur planning to market functional applications would rely on hypotheses of any kind. Similarly, it should be unethical and forbidden for official agencies to consider hypotheses of alleged hazards in support of regulations, especially when such would massively hamper national economies, influence the anxieties, choices and behavior of billions of people, entail heavy penalties and even detention for hapless transgressors. Only hazards and risks certified as materially exceeding no effect thresholds, according to verified experimental evidence, could justify compulsory government regulation.3What is measured is relevant to the hypotheses being tested.Measurements are authentic: what is measured is what is declared to have been measured.Measurements have measured and quantified error rates small enough to ensure observational and statistical consistency.Experimental methods, procedures and tools are relevant and peer-trusted in testing the hypotheses being considered.Known externalities capable of confounding measurements and results are quantified and controlled.Sufficient ground controls allow counterfactual inferences.The experimental record and conclusions are published in peer-reviewed journals.Detailed procedural descriptions and original data are disclosed.Results are reproducible and supported by the above lines of evidence.In obtaining such verified evidence, toxicology would be expected to follow the scientific method. Much has been written about the method’s philosophical underpinnings, but the method and science would be powerless without operational standards to secure reliable measurements and empirical controls: the crucial starting points toward factual knowledge. Aware of this essential need, experimental scientists would recognize the following evidentiary warrants, which must be met as minimum requirements before hypotheses may aspire to become reliable operational knowledge:These standards are not abstruse philosophical propositions, but sensible logical yardsticks implicitly or explicitly observed in common human transactions. Pumping gasoline at a station implies confidence about sequential gallon measurements differing imperceptibly, fuel being genuine and not adulterated, fuel grade being as selected, the price posted being correct, the effects of externalities—such as temperature—being negligible, that such conditions apply to all customers, and so on.Moving to a scientific scenario, precisely assessing the temperature causing water to boil requires first ascertaining the accuracy of the instruments employed, toward being confident of negligible error rates of measurement. Next, experimental controls would require checking the possible influence ambient temperatures and barometric pressure; controlling and specifying the heat source and location, the shape and composition of the vessel in relation to heat sources and other environmental exposures. The purity of the water needs to be ascertained to control the interference of extraneous molecules dissolved. Further observations may include the location of instruments, the amount of water relative to the amount of heat applied, how heat is applied, and several other details.As transparent proofs of honesty, the warrants described are clearly self-evident and the essence of operationally spotless scientific research. Short of them, experiments and observations are stuck as untested conjectures, not to be trusted in enabling functional technologies nor fair policies and regulations. In fact, it is these operational standards of experimental measurement and control which allowed science to accumulate a body of empirical knowledge sufficiently certain to enable the countless technologies currently gracing human lives.The primary experimental and observational data have negligible error rates objectively measurable and explicitly measured.The experimental and observational data supporting the evidence presented are relevant to the issues at hand.The evidence presented has been tested by experimental and observational methods generally accepted by the scientific community.The evidence presented has been subjected to peer-review and publication.These standards have been codified in legal detail by the 1993 Daubert verdict of the US Supreme Court, which defines what scientific evidence is reliable and admissible in federal courts.While compiled for lawyer’s eyes, these criteria accord with the standards of scientific evidence previously listed, and together define what evidence is applicable to sustain defensible policies and regulations.Such standards, however, have not been and are not officially adopted to justify the most costly health, safety and environmental regulations, which instead are imposed by indifferent authority disguised as scientific, but in fact driven by default assumptions contrary to science and objective evidence. As a telling instance, what follows focuses on the official setup of experimental animal tests presumed to identify human cancer hazards, and widely used to justify regulatory activities in the US and around the world.Human testing of presumably hazardous chemical cancer hazards is considered unethical, such testing being mostly run in rats and mice.In fact, such random confounders and other considerations deny the credibility of lifetime animal bioassays as human surrogates for conditions associated with chronic exposures, such as cancer, cardiovascular and neurological diseases, immunity and endocrine disorders and more. Protracted effects in such bioassays evolve randomly over lifetimes and through multiple, incidental, unpredictable, hypothetical and usually unknowable modes of action. Somatic and behavioral conditions, life history, disease sensitivities, hormonal differentials; genetic, epigenetic and immune repair efficiencies; environmental and dietary adaptations and other disparities in different animals, all and more diverge into different causal opportunities ostensibly unique to individual cancer pathologies. Indeed, the outcomes of chronic bioassays are inconsistent across animal species, among strains of the same species, and even among individuals of the same inbred strain.Ultimately, the core problem is the absence of valid methods for an objective translation of chronic animal data into verifiable forecasts of human hazards and risks. Yet, ignoring this blunt reality, lifetime cancer bioassays for the last 65 years have been and are still officially prescribed in the US and worldwide. They support unprecedented regulations imposing $trillion costs to world economies, misguided political and social policies and the unfathomable costs of unwarranted public anxieties, while incapable of showing testable proofs of their public health or environmental utility.The archetypal lifetime bioassay for carcinogens is a standardized test in rats and mice, requiring over 3 years at a cost of several $million per test. Its use follows the 1958 Delaney Clause of the federal Food, Drug, and Cosmetics Act, prohibiting food additives if they cause cancer in animals by appropriate tests. The statute is silent about what makes appropriate tests, and regulators responded by unilaterally adopting default bioassays in rodents. They were soon challenged, because—as just described—bioassays yield widely contrasting results depending on animal species and inbred strain choices, design, setup, conduct and interpretation rules. Decades of discussions and trials have finally come to accept the overwhelming evidence excluding the feasibility of chronic animal tests to factually infer cancer hazards and risks in humans.The intent of the Delaney clause was rapidly extended beyond foods without statutory authorization, to regulate every conceivable exposure in lifestyles, housing, agriculture, medicine, communications, transportation, industry, defense and more. More poignant still, such practices are not exclusive to the US but also migrated to the world, sharing a common philosophy, assumptions and terminology and becoming a regulatory praxis of the World Health Organization.To perceive hands-on the roots of those massive interventions, let it be repeated how cancer bioassays yield different results depending on their set up and conduct, what animal’s numbers and susceptibilities, what exposures and doses, diets, environmental conditions, diseases and more. In fact, shortly after the Delaney clause became law, the US Food and Drug Administration (USFDA) found traditional staple foods to be causing cancer in animal tests. Prohibiting basic constituents of human diets would have been unthinkable, and the agency was forced to secure from Congress a dispensation for a special category of products defined as Generally Recognized as Safe (GRAS). Such clear warning about the inevitably arbitrary makeup of any chronic animal test was ignored when other agencies—notably the US Environmental Protection Agency (USEPA)—embarked into massive regulatory programs in the early 1970s, unilaterally seizing the Delaney mandate as their principal instrument.As the core example for other agencies, the USEPA guidelines to initiate and run lifetime bioassays in rats and mice have been issued without legislative concurrence, imposing unilaterally a standard set of arbitrary default assumptions. The agency openly admits such assumptions are designed to facilitate its regulatory agenda by maximizing positive cancer responses in animals, offering the oblique justification of acting under a mandate to regulate by proactive precaution.The most significant default assumptions are as follows: inbred rats and mice must be accepted as equivalent proxies of freewheeling humans; bioassay must be run for the lifetime of the animals at maximum tolerated doses (MTD), which are expected to generate a chronic general toxicity measurable as a lifetime 10% decreased body weight gain; bioassay interpretation must assume that excessive lifetime MTD animal exposures represent humans exposed to much smaller doses and at fractional durations; the metabolism in animals at MTD is assumed equivalent to the metabolism of humans at low doses and exposure duration; benign lesions are assumed equivalent to malignant ones; the route of exposure is assumed to be irrelevant; linear extrapolations from high MTD doses in animals to lower doses in humans must be used. The statement “Animal studies are conducted at high doses in order to provide statistical power” disguises the agency resolve to artificially maximize cancer instances under MTD conditions. The clear intent is to produce cancer signals by whatever means, aiming at precautionary regulations.“…a correlation between carcinogenicity in animals and possible human risk cannot be made on a scientific basis.” In 1981, Dr. David Rall, first director of the National Toxicology Program, testified in congress that human cancer risks could not be derived scientifically from animal tests, insisting that Congress and the public ought to have faith (sic) in experts who comb rat and mouse entrails to divine human cancer hazards. A 2009 committee report of the US National Academy of Sciences reads “… the defaults involving science and policy judgments, such as the relevance of a rodent cancer finding in predicting low-dose-human risk, are used to draw inferences ‘beyond the data’, that is beyond what may be directly observable through a scientific study.”10Imposed with the force of law, such assumptions have reduced chronic regulatory bioassays into a formulaic paradigm inhibiting introspection, while openly admitting the absence of objective, truthful and relevant evidence in the entire process. The International Agency for Research on Cancer, an arm of the World Health Organization, conceded 50 years ago ... risk management decisions continue to be made by state and federal agencies; however it is not known whether the decisions being made are health protective.” While those decisions could also be damaging to public health, the Academy’s committee and regulatory agencies appear fully aware that science and commonsense preclude lifetime bioassays in animals to be predictive of human cancer hazards and risks.In the same document (p.17), the Academy’s committee concluded “Be as it may, bioassays are only a first step in the current process of regulating alleged cancer risks. The second move is the mathematical extrapolation from lifetime MTD data in rodents to the lower natural exposures in humans, raising the question of what mathematical model should be appropriate for the task. Decades ago, US regulators fitted lifetime animal MTD data to various mathematical models such as the Benchmark dose, One hit, LMS, constrained LMS, maximum-likelihood LMS, Weibull, constrained Weibull, Logistic, Probit, Poisson and more models and variants. Each model represents a different x/y distribution and a specific plot or graphic visualization of its mathematical function.Impasses soon emerged, for there are no logical or mechanistic grounds to prefer one model over others, while the dose data of any MTD bioassay could be made to fit most any model. Different models, however, lead to low-dose extrapolations differing by several orders of magnitude. Facing a choice, the USEPA imposed the linear model as the default assumption to be used, not because of any objective rhyme or reason but because it seemed the closest to suit agency policy, while disguising arbitrariness with mathematical sleight.Pained to appear dictatorial, regulatory agencies in the US and around the world also have sought to achieve an aura of consensus by submitting bioassay and modeling records to advisory committees. By appointing such committees as advisory but without executive clout, regulators have aptly managed dissenting opinions internally while freely pursuing their interest in proliferating alleged human cancer hazards.A final injection of bias comes with the quantitative interpolation of safety factors during rulemaking. Naturally arbitrary, such factors contribute a major obfuscation, as they escape scrutiny under the seduction of precaution and the hollow authority of precedent. They are numerical adjustments interpolated during the final editing of a rule according to political checks and balances, and toward ruling for the minimal exposures compatible with projected uses. Obviously, such minimal exposures could be defined without default bioassays, magic dose–response models, advisory committees and safety factors. Defined by a balanced analysis of factual commonwealth benefits, minimal precautionary exposures could result from an accounting process pragmatic and transparent enough to be an effective discipline to market ambitions and a safeguard for public health.In summary, currently enforced chronic bioassays to define human cancer hazards and risks do not meet the evidentiary standards of experimental science or the Daubert standards of admissible evidence. They are run in irrelevant animal models, according to irrelevant and intentionally forced experimental designs and default assumptions, do not control for externalities, and their results are manipulated by arbitrary mechanistic models, perfunctory advisory committees and safety factors.In a curious turn of events, the alleged cancer hazards produced by the USEPA have been embraced as their flags by assorted advocacy groups, making the agency a super-advocacy organization with the awesome power of regulating with the force of law.As noted, with few rhetorical modifications, these very practices found acceptance with regulators worldwide, after earlier adoptions by the International Agency of Research on Cancer (IARC), an agency of the World Health Organization (WHO), and by the major economic blocks of the European Union, Japan, China and South America. Most scientists have embraced such practices without protesting, even though lesser transgressions have led to harsh punishments for misconduct in their ranks. More remarkable is how societies pretending to be free, rational and enlightened, have been pressed to countenance and fund such openly misleading, costly and fruitless practices at the highest levels of public policy. Even more remarkable is how legislators of free democratic nations could overlook obvious conflict of interest ambiguities in statutes setting up and motivating regulatory agencies, thus enticing regulators into deceptive gambits.The natural aspiration of US government staff has been to operate autonomously under the traditional and autocratic Crown-endorsed authority of most European bureaucracies: a model the US constitution disallows. Such aims arose repeated concerns, more recently during the administration of Franklin D. Roosevelt, when numerous executive agencies with independent authorities were set up to sustain New Deal policies, and more after Roosevelt vetoed the 1940 Walter-Logan bill calling for court supervision of those agencies. The onset of WW2 delayed further discourse, soon revived with the passage of the 1946 Administrative Procedure Act (APA).12ancien régime” practices the US Constitution excludes. The APA briefly disposes of the central issue of what evidence can justify the agencies independent promulgation of legally binding regulations, offering this equivocal directive:Except as otherwise provided by statute, the proponent of a rule or order has the burden of proof. Any oral or documentary evidence may be received, but the agency as a matter of policy shall provide for the exclusion of irrelevant, immaterial, or unduly repetitious evidence. A sanction may not be imposed or rule or order issued except on consideration of the whole record or those parts thereof cited by a party and supported by and in accordance with the reliable, probative, and substantial evidence.”13“In principle, the APA details the constitutional behavior expected from government agencies, but a close examination shows key agencies endowed with independent legislative, enforcement and judicial authority, further protected by the deference of state and federal courts. Feeling sovereign, the agencies’ behavior is more in line with those “irrelevant, immaterial, or unduly repetitious evidence”, nor of what may constitute “reliable, probative, and substantial evidence”. In essence, agencies are given carte blanche in gathering whatever evidence suits their policy, as it happened with the imposition of default assumptions, the choice of extrapolating mathematical models, the selectively appointed review committees, and the insertion of safety factors capping the abusive “regulatory science” of alleged human cancer hazards.Contrary to constitutional writ, the Act admits the autonomous authority of agencies to issue rules and orders with the executive force of laws promulgated by Congress, burying unresolved Constitutional issues. Further, the Act identifies proponents of rules and orders from outside of agencies, but does not touch on the more frequent reality of rules and orders originating within the agencies and teeming with conflicts of interest. It also fails to provide guidance on what may authorize the exclusion of “Challenging agency rules is notoriously difficult, for US agencies also have their own administrative courts first in line to adjudicate external grievances, have their own prosecutors and judges with the power to issue fines and detention, and a fully armed police ready for action. An external party failing to obtain relief in administrative trials can challenge agencies in federal courts, but success has been extremely rare. Still, it is encouraging to find the APA listing those agency misbehaviors a court would find unlawful enough to:... set aside agency action, findings, and conclusions found to be (1) arbitrary, capricious, an abuse of discretion, or otherwise not in accordance with law; (2) contrary to constitutional right, power, privilege, or immunity; (3) in excess of statutory jurisdiction, authority, or limitations, or short of statutory right; (4) without observance of procedure required by law;(5) unsupported by substantial evidence....; or (6) unwarranted by the facts... ″.14“On these counts, US agencies’ actions should have been found illegal long ago in the regulation of alleged human cancer hazards based on irrelevant animal tests, yet those regulations remain enforced today. As noted, following the bioassay assimilation in WHO protocols, homologous regulatory practices expanded worldwide. If not enough, US federal regulatory programs metastasized into smaller competitive agencies with similar regulatory authority at state, cities and municipalities, vastly extending a mindset of autonomous administrative authority.Clearly, the majority of people lack the tools and time to critically understand the current regulatory process, which has been endured as a matter of faith, likely for the same reasons the emperor is not seen as naked in the fable. In reality, a regulatory alliance appears to have anesthetized the collective conscience of nations into accepting whatever dictates are imposed under illegal claims of superior knowledge and authority.Why illegal? Again, regulations in most democratic societies are equivalent to laws and laws can only be issued by freely elected legislative bodies. Legislators permit government agencies to impose regulations if based on evidence generally recognized as scientifically verified. Legislators grant this permission expecting to concur with such evidence and to act accordingly. By contrast, compulsory regulations would be illegal if issued autonomously by agencies on the basis of arbitrary judgments. Most recently, the US Supreme Court reaffirmed this doctrine, finding the USEPA acted illegally in setting arbitrary standards on greenhouse gas emissions from power plants: a weighty judgmental appraisal which the Court found to be the prerogative of legislators and not of unelected operatives.15This verdict of the US Supreme Court ratifies the illegality of arbitrary justifications of massive regulatory burdens in the US, from default assumptions to safety factors and other imposed elements of risk assessment. Principal among these elements is the overused and supremely judgmental invocation of precaution, whose pervasive consequences are the central pretext for the self-serving illusory regulations of alleged human hazards on many fronts, from carcinogens to endocrine disruptors, pandemic management, global warming, and more.At large, this US Supreme Court ruling carries wide implications, for a cascade effect could upset much of the regulatory house of cards in the US and possibly worldwide. An earlier effect of this decision would be reminding legislators of their duty to critically assess whether regulations are justified by reliable evidence or by precautionary judgment, which could alarm legislators aware of their limited technical experience. A sensible solution would be requiring agencies to justify regulations by following legislator-approved evidentiary rules, thus relieving legislators from detailed technical hearings. In fact, such evidentiary rules are readily available in the Daubert decision of the US Supreme Court, which outlines the criteria of reliable evidence: the litmus test of legal and acceptable regulations.ipse dixit” dogmatic experts who dominated judicial history, instead imposing the adoption of tested scientific evidence as the exclusive grounds of expert testimony. Daubert settled the first official confrontation of the rhetorical ways of a millennial legal tradition versus the testable evidentiary power of experimental science. Science won with Daubert, making for a crucial civilizing advance in human affairs.Notably, the Daubert ruling has been hailed as a turning point in liberating court proceedings from “Future commentators could not fail to marvel at the public quiescence facing today’s profoundly corrupt regulatory apparatus, hinged on alleged cancer hazards. Are we the same who send astronauts to the moon, develop wondrous electronics, conquer diseases, create the means to affordable food for billions, and much more? Change is overdue, for the just described regulatory arrangement is the ultimate breach of trust in government.It would be presumptuous to chart a detailed course of remedial action, likely to happen by fits and starts, encouraged or repressed by evolving social and political models. Yet, it is possible to think of interventions toward testable and truthful evidence supporting fair regulations in health, safety, the environment and other scenarios as well.18“…set aside agency action, findings, and conclusions” and should carry enough authority to dismiss the absurd construct now regulating alleged cancer hazards. Sensibly upgraded, the Act could have immediate effect by instructing regulatory agencies to exclusively adopt Daubert’s rules of evidence, integrated with the criteria of scientific evidence listed earlier in this paper, as the central effort toward a transparent and accountable government.As noted, the US Administrative Practice Act list of agency misbehaviors would be sufficient to Such an approach would eliminate the illicit use of animal bioassays and the ensuing regulations, and force to reassess the current administrative setup presiding over the delusions of what is passed as regulatory science. As a desirable corollary, separate legislation protecting ethical transparency should also attempt to moderate or counter advocacy claims of alleged hazards apt to inflame unwarranted public anxieties. In the US, such claims and anxieties may prove inevitable in the superior context of constitutionally protected speech. Still, crying fire in a crowded theater without true evidence cannot be protected, which could justify requiring advocacies to sustain their claims under Daubert rules of evidence, or to underscore their hypothetical vagueness.Regulations backed by independent and testable scientific evidence, derived from acute and sub-acute tests in animals and humans, and from human epidemiologic studies counterfactually verified; andPrecautionary regulations backed by utility considerations to permit only the least exposures necessary for effective uses, when hazards are only supposed and factual evidence is not achievable.A general response for health, safety and environmental regulations would adopt separate guidelines for:In this last context, a stark but obvious reality postulates the impossibility of life without hazards and risks, measurable or not, coupled with the obvious calamities of uncompromising precaution. It should be sensible to trade off the costs and uncertain safety of precaution versus the public health advantages of an enterprising commonwealth. At the same time, due to their exclusively judgmental makeup, precautionary regulations will likely require major input from elected legislators. As noted, statutory accounting guidelines could direct regulators in exploring precautionary opportunities, which would require new administrative staff training and expertise. A policy of precaution could be further assisted by a national epidemiologic surveillance program to monitor early signs of possible adverse effects in heavily exposed cohorts, similar to what is currently done in the pharmacovigilance of medicines and medical devices, and in occupational surveillance.Additional remedies may include a Constitutional clarification of government authority and behavior in service to the public, a code of personal responsibility for government operatives of executive level, and expanding agency oversight by an independent judicial entity or an impossibly neutral executive agency. Moving in these directions would reaffirm the superior integrity of science and restore public trust in government, regulations and policies. The odds it may happen soon are low, for standing legal and bureaucratic constructs would put forth a mighty inertia in defending the indefensible status quo. Tied to current regulations, so numerous structural and personal economies are bound to resist changes to lifestyles, professional and institutional standings, records of past conduct, beliefs and pride.Regulated industries—big names especially—also may resist, for compliance with an imposed regulatory ritual has continued to secure profitable marketing under however unwarranted government authority. Change could also upset the industrial reliance on the ritual’s high cost as an effective tool to moderate competition. Passionate resistance also could be expected from the Torts Bar, far too successful in riding the current arbitrary regulation of alleged hazards. Resistance could also arise worldwide, where illusory US practices were adopted uncritically and aggressively by WHO agencies and a majority of regulatory operators.Yet, sooner or later, the current setup must change, since government impositions based on fabricated claims of objectivity cannot stand unchallenged in societies claiming and desirous to be enlightened and free. The vast human and material resources now wasted by regulations without verifiable social, public health and environmental benefits ought to be made productive, sustained by the discipline of science and the pledge of fairness, reason and citizen sovereignty, first set forth by “We the People of The United States” in their Constitution.


The World Health Organization recognises injuries as a growing global public health problem. While there are several causes of injuries and trauma, relevant research is mostly centred on road traffic accidents, burns, and drowning with less focus on violence-related injuries/trauma such as sexual and gender-based violence (SGBV). To identify priority research topics, prioritisation, and development of practice guidelines to mitigate the impact of injuries/trauma resulting from SGBV, this systematic scoping review will aim to map and describe the range of research relating to injuries/trauma due to SGBV in the global context.A scoping review guided by Arksey and O’Malley’s methodological framework will be conducted. Literature relating to injuries/trauma and SGBV will be searched in PubMed, SCOPUS, and PsycINFO, CINAHL, Web of Science, Google Scholar, Trip, guideline repositories, websites, and reference list of included articles. This study will include evidence sources focused on the epidemiological burden, guidelines for out-of-hospital and in-hospital care of victims, barriers or facilitators to reporting and obtaining healthcare, and approaches for mitigating injuries/trauma due to SGBV. The search will be limited to publications within 10 years (2012 to 2023). Two authors will apply the eligibility criteria to identify potentially relevant citations. The data will be extracted in duplicate and methodological quality assessed using varied tools . The study will be reported in keeping with the Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews.The scoping review will highlight existing literature on injuries/trauma due to SGBV and identify gaps to facilitate research prioritisation, development of guidelines, and resource allocation to alleviate the impact of injuries/trauma resulting from SGBV. This study’s findings will be disseminated via a series of meetings with key stakeholders  in the field of healthcare, policy, social welfare, GBV interest groups, and others. Also, the final scoping review results will be presented at relevant workshops, meetings, and conferences. Moreover, this study’s findings will be disseminated via journal publications and policy briefs.The online version contains supplementary material available at 10.1186/s13643-023-02345-8. The World Health Organization (WHO) reports that injuries are a growing global public health problem . In 2021Gender-based violence also referred to as sexual and gender-based violence (SGBV), is any damaging act of sexual, physical, psychological, mental, or emotional abuse that is performed against a person’s will, and is based on socially attributed (i.e. gender) disparities between males and females –9. EvideThe consequences of SGBV may include serious short- and long-term effects such as physical, mental, sexual, and reproductive health problems for women survivors and their communities –16. PhysThe WHO advocates for countries to generate evidence on what works and on the magnitude of the problem by carrying out population-based surveys or including violence against women in population-based demographic and health surveys . The WHOTo achieve this scoping review objective, the Arksey and O’Malley methodological framework  will be What evidence on the epidemiological burden of injuries/trauma due to SGBV in the global context over the last decade exists?What guidelines are available or absent for the management of SGBV victims with injuries/trauma  in the hospital and out-of-hospital settings?What are the reported barriers and facilitators to reporting and obtaining healthcare by victims of SGBV with injuries/trauma?What evidence on approaches used to mitigate injuries/trauma due to SGBV exist?This scoping review question will be what research evidence on injuries/trauma due to sexual and gender-based violence within the last decade exists in the global context? The Population, Concept, and Context framework  used to The search will aim to identify relevant peer-reviewed papers to answer the review questions. To achieve this, we will conduct a comprehensive search in the following electronic databases from January 2012 to the search date in 2023: PubMed/MEDLINE, SCOPUS, CINAHL, PsychINFO, and guideline clearinghouses . We will further search the WHO website, relevant Ministries, Departments, and Governmental websites as well as UNFPA website for relevant literature. Google Scholar search engine will also be used to search for relevant literature. A search strategy developed in consultation with an information scientist will be employed for database literature searches. The search string will involve a combination of keywords , Boolean operators (AND/OR), and Medical Subject Heading (MeSH) term  Version 2018  will be For data abstraction, a spreadsheet will be created and pilot-tested with 10% of the included evidence sources. To ensure that the form captures all relevant data to answer the review question, the necessary changes will be made. Following a thorough reading of the full texts, two reviewers will independently extract all relevant data from the included evidence sources. The data from the included evidence sources will be extracted using a hybrid approach that combines inductive and deductive reasoning . Due to Developer Date/date last updatedPublisherCountry where the guideline was developedGuideline titleGuideline aim/objectiveGuideline question(s)Targeted populationTopic/type of injury/traumaTargeted userRecommendation(s)Development/adolopment approach .Evidence grading system or toolRigour of development (AGREE-II Domain 3 components)Strength of recommendationsA descriptive analysis will be conducted to describe the themes (topics) aligned with this scoping review’s question based on initial coding and categorisation . RelevanThis systematic scoping review aims to map and describe the range of research relating to injuries/trauma due to SGBV in the global context in order to identify priority research topics, prioritise, and develop practice guidelines to mitigate the impact of SGBV injuries/trauma. Injuries are a growing global public health problem. Injuries occur due to a variety of causes; however, research on injuries/trauma is primarily focused on traffic accidents, burns, and drowning, with less emphasis on violence-related injuries/trauma such as sexual and gender-based violence (SGBV), hence informing this study. This scoping review will be limited to research evidence within the last decade. Focusing on more recent publications will allow the study to capture the most up-to-date information and research findings and ensure that the review is relevant and reflects current knowledge, practices, and what interventions might be needed. It is hoped that this study will highlight existing literature on SGBV injuries/trauma and identify gaps to facilitate research prioritisation, development of guidelines, and resource allocation to reduce the impact of SGBV injuries/trauma.This study’s findings will be disseminated to the scientific community through traditional means such as publications in relevant peer-reviewed journals and presentations at relevant conferences. Findings will be shared with local and international policymakers and practitioners through targeted stakeholder meetings or workshops, policy briefs, and a webinar. The findings will also be shared with the general public or the non-scientific community through infograms, online, blogs, print media, and radio, and webinars.Additional file 1: PRISMA-P Checklist.