What are evidence-based strategies and how can public health practitioners find evidence without conducting extensive literature reviews? We developed an inventory of clearinghouses and other resources that disseminate research on evidence of effectiveness. We examined differences in evidence classification among 6 evidence clearinghouses that rate the effectiveness of community-level strategies to address determinants of health. Most evidence clearinghouses clearly defined their scope, but only a few clearinghouses explicitly defined the types of strategies they assess . The term “evidence-based” was widely used, but definitions and standards were inconsistent across organizations and disciplines. Evidence clearinghouses varied in the way they used evidence rating classifications and criteria for assigning ratings. Attention to detail is important. The criteria for the top rating of some evidence clearinghouses, for example, require a more thorough literature review with more robust results than the criteria for the top rating of others. In addition, some clearinghouses report only on strategies considered to be evidence-based, whereas others also report on strategies that have no effect, mixed evidence, or no qualifying studies, demonstrating that a listing of a strategy by an evidence clearinghouse does not necessarily mean that it is effective. We conclude by providing guidance for users of evidence clearinghouses about how to interpret and effectively apply rating criteria across platforms: look closely at the details of how clearinghouses assign their ratings and be aware of similarities and differences when you are aligning potential strategies with your local priorities. We encourage communities to balance evidence with local needs, resources, and culture in strategy selection and funding decisions. Public health practitioners are increasingly aware of the importance of considering evidence about effectiveness when selecting strategies for implementation to improve community health.This report offers an inventory of evidence clearinghouses for disseminating research on evidence and a review of the approaches used by a subset of these clearinghouses that provide summary ratings of evidence.Understanding the types of strategies these clearinghouses review and how they develop their summary ratings is key knowledge for public health practitioners to make informed decisions about potential strategies for implementation.Preventing Chronic Disease. Medscape, LLC is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.In support of improving patient care, this activity has been planned and implemented by Medscape, LLC and Medscape, LLC designates this Journal-based CME activity for a maximum of 1.00 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 1.0 MOC points in the American Board of Internal Medicine’s (ABIM) Maintenance of Certification (MOC) program. Participants will earn MOC points equivalent to the amount of CME credits claimed for the activity. It is the CME activity provider’s responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit.Release date: September 5, 2019; Expiration date: September 5, 2020Upon completion of this activity, participants will be able to:Describe how evidence clearinghouses rate evidence, according to a report and reviewDetermine lessons learned from reviewing a sample of clearinghouses’ evidence of effectiveness ratings, according to a report and reviewIdentify guidance needed by public health practitioners, community leaders, and policy makers to be informed consumers of evidence clearinghouses that summarize evidence about health improvement efforts, according to a report and reviewEDITOREllen Taratus, MSEditorPreventing Chronic DiseaseDisclosure: Ellen Taratus, MS, has disclosed no relevant financial relationships.CME AUTHORLaurie Barclay, MDFreelance writer and reviewerMedscape, LLCDisclosure: Laurie Barclay, MD, has disclosed no relevant financial relationships.AUTHORSAlison Bergum, MPAPopulation Health InstituteUniversity of Wisconsin–MadisonDisclosure: Alison Bergum, MPA, has disclosed no relevant financial relationships.Lael Grigg, MPAPopulation Health InstituteUniversity of Wisconsin–MadisonDisclosure: Lael Grigg, MPA, has disclosed no relevant financial relationships.Marjory L. Givens, PhDPopulation Health InstituteUniversity of Wisconsin–MadisonDisclosure: Marjory L. Givens, PhD, has disclosed no relevant financial relationships.Bridget Booske Catlin, PhDPopulation Health InstituteUniversity of Wisconsin–MadisonDisclosure: Bridget Booske Catlin, PhD, has disclosed no relevant financial relationships.Julie Willems Van Dijk, PhDWisconsin Department of Health ServicesMadison, WisconsinDisclosure: Julie Willems Van Dijk, PhD, has disclosed no relevant financial relationships.Since the early 1990s, evidence-based decision making has gained prominence in the field of medicine, followed by the field of public health. In medicine and public health, evidence typically refers to research evidence, rather than experiential or contextual evidence . Our stuSystematic reviews of randomized control trials (RCTs) are widely recognized as the gold standard of intervention research. Such reviews follow an established process for searching, critically appraising, and summarizing results of research studies, accounting for all relevant qualifying studies and their results and establishing whether research findings are consistent and generalizable across populations and settings. Individual studies  vary in strength of evidence. Sometimes, however, no study is available, and practitioners might turn to expert opinion ,4. ReseaCommunity leaders and practitioners have numerous approaches to finding evidence and varying criteria for considering evidence in decisions . UnderstSearching the scientific literature for RCTs or other studies is often not feasible for public health practitioners or community members, largely because of limited time and access to scientific literature . EvidencWe developed a comprehensive, but not exhaustive, inventory of evidence clearinghouses and other resources that summarize evidence on strategies that address the multiple determinants of health . We focuEach clearinghouse has its own scope of interest, methods, and rating classifications. Many clearinghouses also provide additional content to accompany evidence ratings and support effective decision making. Some, such as The Community Guide, WWFH, and Social Programs that Work (SPTW), provide cost-related information. This information ranges from The Community Guide’s economic effectiveness analysis (conducted for strategies they rate as “recommended”) to study details noted by WWFH and SPTW. Some also emphasize tools or content that can bolster efforts to increase equity or reduce disparities in health-related outcomes. WWFH, for example, assesses the likely effect of each strategy among socioeconomic, racial/ethnic, and geographic groups. Many clearinghouses that assess and rate evidence also provide examples, stories, or other action-focused resources to support implementation.To understand how evidence clearinghouses rate evidence, we selected a sample of clearinghouses that provide evidence of effectiveness ratings for strategies that affect multiple determinants of health. Multiple determinants of health are defined in several ways, for example, “genetics, behavior, social circumstances, environmental and physical influences, and medical care” . The CouOur focused review examined the work of the following 6 evidence clearinghouses: Best Evidence Encyclopedia (BEE); The Community Guide; Healthy Communities Institute (HCI); Rural Health Information Hub (RHIhub); SPTW ; and WWFH.We conducted a qualitative analysis of the scope, methods, and ratings as described on the website of each of the 6 selected clearinghouses, with particular attention to the literature assessment , the criteria used to assess the quality of individual studies, and the type and number of studies required to establish each rating. We also considered scope of interest and the types of strategies assessed. We completed reviews in September 2018 and confirmed our information in October 2018. We invited staff members from each of the 6 clearinghouses to provide feedback on the accuracy of our information.Each evidence clearinghouse has its own scope of interest . The typScope of interest and type of strategies assessed. WWFH, HCI, and The Community Guide address multiple determinants of health; the latter two also address several diseases and injuries. RHIhub focuses on programs and interventions in rural communities. SPTW focuses on social programs and BEE on education programs. Some, such as The Community Guide and WWFH, emphasize broadly defined policy, systems, and environmental change (PSE) strategies; WWFH also includes some named programs, such as Nurse Family Partnership and Reach Out and Read. Other clearinghouses, such as SPTW and HCI, focus more heavily on named programs.Approach to compiling and assessing literature. The websites of these 6 clearinghouses indicate various approaches to compiling and assessing available literature in support of their evidence ratings. The Community Guide and SPTW conduct systematic reviews, and BEE conducts systematic reviews with meta-analysis. WWFH conducts an extensive literature review, informed by the principles of systematic review methods, to capture and assess available evidence in a shorter time frame than systematic reviews, allowing inclusion of more strategies than the aforementioned clearinghouses. HCI and RHIhub seek and accept submissions from evaluators, practitioners, and others, fostering dissemination of early practice-based results. Review criteria for submissions were not apparent in our search of these 2 websites; it was also unclear whether a formal literature review process is used to inform evidence ratings.Study types considered. Studies vary in their ability to determine causality; reviewed clearinghouses vary in the types of studies required to support evidence rating assignments. SPTW and BEE include RCTs and strong quasi-experimental designs (QEDs) as the foundation for their rating assignments. The Community Guide and WWFH include RCTs, QEDs, and some weaker study designs in their reviews. Strong QEDs are based on sound theory, use comparison groups, and typically include multiple measurement points; weaker study designs are also based on sound theory but do not have comparison groups and might not include multiple measurement points  might not be so well defined. Understanding the scope and types of strategies covered can help users search appropriately for strategies to address local priorities.Ascertain what constitutes “evidence-based” for each clearinghouse, because no consensus exists. Among the clearinghouses we examined, there is no universal definition of “evidence-based.” Clearinghouses vary in the terminology they use to describe levels of evidence and effectiveness and the criteria used to assign their ratings. Although evidence clearinghouses provide a streamlined way to learn about evidence, it is important for practitioners to pay attention to how each clearinghouse defines each term used in their rating classifications.Understand that evidence clearinghouses weight research designs differently. Some, but not all, clearinghouses give greater weight to evidence from systematic reviews, RCTs, and strong QEDs than to other study types, particularly in their highest evidence rating categories. Systematic reviews and RCTs are recognized as the gold standard of effectiveness; seeking out interventions with this level of evidence can be important when a community is scaling up an intervention or investing substantial time or money, or when political stakes for success are high.Recognize differences in evidence clearinghouses’ requirements for literature review and their considerations of study quality and quantity. Some clearinghouses search for evidence more systematically and judge study quality and design more strictly than others. Some also emphasize replicability more heavily. Yet others focus more on dissemination of early practice-based results. Understanding the breadth and replicability of studies provides practitioners with critical information as they consider deploying interventions in their own community.Be aware that most evidence clearinghouses do not assign ratings for ineffectiveness, expert opinion, or mixed results. Only 2 clearinghouses that we examined closely include information about strategies with evidence of ineffectiveness, and WWFH is the only one that has the category “expert opinion.” Exploring evidence along the entire continuum of effectiveness can provide practitioners with information about ineffective policies or programs that might need to end, strategies with mixed evidence that may need a closer look, and strategies rated “insufficient evidence” or “expert opinion” that may especially benefit from more rigorous evaluation designs.In general, more focus appears to be on what works rather than on what does not or is unknown. This discrepancy is likely due, at least in part, to the fact that more literature is available for what works than what does not — partially a result of publication bias . This foWhat knowledge do community leaders and policy makers need to be informed consumers of evidence clearinghouses that summarize evidence about health improvement efforts? As demonstrated in our qualitative review of publicly available data and in a 2016 assessment of education-related evidence resources, “the methods used in these syntheses vary in fundamental ways” (Caution should be taken in implementing strategies that are found to have no effect or mixed results; communities interested in such strategies should consider study results, possible modifications to the strategy, and implications of implementation fidelity. Strategies for which literature reviews yield no qualifying studies might simply be too new to determine likely effectiveness. In these situations, conducting a pilot or implementing a rigorous evaluation to be sure that these strategies do, in fact, achieve expected outcomes is a wise approach.Finally, evidence clearly matters to decision making but so do other factors. Knowledge building is a continuous process, and the creativity of local communities in addressing perplexing challenges, accompanied by a “test and see” approach, is often a source of new evidence. Local culture, potential effect on disparities, feasibility, and cost, are also important considerations. Purposeful approaches to balance these factors, along with evidence of effectiveness, can best support efforts to select strategies that will appropriately address local priorities.


CATSPER2 male infertility gene. However, given the low resolution of CMA at this homologous locus, it was unclear if the neighboring STRC hearing loss gene was also affected. Therefore, we developed a novel allele‐specific PCR strategy, which narrowed the proximal breakpoint of the maternally inherited deletion to a 310 bp interval that was 440 bp upstream from the STRC transcription start site.Here, we report the prenatal detection of a compound heterozygous deletion at chromosome 15q15.3 by clinical chromosomal microarray (CMA) testing that included the  To The Editor:https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss). Approximately 70% of affected individuals have nonsyndromic hearing loss (NSHL), which currently has over 115 recessive, dominant and/or X‐linked genes implicated in its molecular pathogenesis (https://hereditaryhearingloss.org/). The DFNB1 locus (GJB2 and GJB6) accounts for ~50% of severe to profound congenital NSHL cases, followed by USH2A, STRC, and MYO15A  distal pseudogene (pSTRC) at chromosome 15q15.3 makes it very challenging to specifically interrogate STRC by most molecular assays. The directly oriented ~100 kb segmental duplications that encompass STRC and pSTRC can also act as substrates for nonallelic homologous recombination, which leads to the formation of recessive STRC deletions as well as the reciprocal duplications  have very low resolution at this locus, particularly across the 28 exons of STRC  strategy.Importantly, the extensive homology across the CATSPER2 [reported as arr[GRCh37] 15q15.3(43916972_43950720)x0]  and ddPCR  detected an apparently homozygous 33.7 kb deletion of chromosome 15q15.3 that included STRC (NM_153700) and CATSPER2 (NM_172095). Primers were designed using Primer 3 web version 4.0  and all amplicons were examined on 1% agarose gels. As expected, all 14 specific targets were successfully amplified from parental DNA given the presence of at least one intact copy in both parents; however, AS‐PCR amplicons 8–14 were absent in fetal DNA  for a second round of AS‐PCR of seven separate amplicons (163–1623 bp) using nonunique 3' primers x1], which was considered paternally inherited.To further resolve the proximal breakpoint of the maternal deletion, a novel two‐tiered AS‐PCR strategy was developed. The first round of AS‐PCR had 14 unique amplicons across a ~110 kb interval covering both Given these test results, the couple was provided reassurance regarding hearing loss. The baby girl was born naturally at 39.5 weeks after an uncomplicated pregnancy and passed the newborn hearing screen. Importantly, no concerns have been noted on clinical follow‐up, and at 4 years of age, she currently is thriving with excellent hearing and normal development.STRC and CATSPER2 deletions , all currently available clinical assays have low resolution across this region, which is considered one of several disease regions in the human genome that is not amenable to direct interrogation by hybridization‐based assays and/or short‐read sequencing  across selected disease genes to better inform phenotype prediction and prenatal genetic counseling.Although many different molecular platforms are used to detect L.S., L.E., and S.A.S. are paid employees of Sema4, a Mount Sinai venture, Stamford, CT.


Healthcare decisions should be based on all relevant evidence.Network meta-analysis (NMA), also termed multiple treatment meta-analysis or mixed treatment comparisons, was developed as an extension of pairwise meta-analysis to allow comparisons of more than two interventions in a single, coherent analysis of all the relevant RCTs.The underlying idea is very simple: consider three friends, Anne, Ben and Charles. If we know that Ben is 7 cm taller than Anne, and that Charles is 10 cm taller than Anne, then we know that Charles is 3 cm taller than Ben, and is therefore the tallest. We can also rank the friends in terms of who is tallest as 1=Charles, 2=Ben, 3=Anne. So, by taking Anne’s height as reference and measuring the heights of the others compared with hers, we know how everyone’s height compares to each other and how to order the friends by height. The only assumption being made is that the heights we measured are an accurate reflection of the true heights of the three friends . It is easy to see that the same relative heights and ranks would be obtained if one of the male friends had been the reference, and how the height relationships would extend if more than three friends had been measured. This is exactly how NMA works, although we also take the uncertainty  in the relative effect estimates into account, as is standard in meta-analysis.indirect comparison of treatments C and B,consistent, that is, they must add up in the same way as the friends’ heights. In other words, the MD of C compared with B must be the difference between the MDs of C versus A and B versus A, which is −1.7 units. This consistency relationship, sometimes also called transitivity, must hold if the studies are estimating the true effects in a patient population. To obtain a 95% CI for the C versus B comparison, we simply add the variances of the two other comparisons to obtain an SE with which a 95% CI can be constructed: (−3.01 to –0.39). This type of comparison is termed ‘indirect’ as it relies on evidence against the comparator A, and not on ‘direct’ head-to-head evidence from one or more trials of C versus B.Suppose we are interested in comparing treatments B and C. We find one trial comparing B with A, giving an MD of −2.3 with an SE of 0.45 and one trial comparing C with A giving an MD of −4 with an SE of 0.5. This suggests that both treatments B and C are better than A with 95% CIs that exclude no effect: (−3.18 to –1.42) for B compared with A and (−4.98 to –3.02) for C compared with A . The network formed by these comparisons is given in Suppose now that we also had evidence from a new study on the same patient population which compared treatment C with B, giving an MD of −1.8 95% CI (−3.66 to 0.06). Traditional hierarchies of evidence state that estimates from direct head-to-head RCTs provide the ‘best available’ evidence of intervention effects. Should we now discard the indirect evidence? Or perhaps we should prefer the indirect evidence since it suggests a statistically significant effect? To do either is contrary to the principle of using all relevant evidence for decision making.NMA relies on the same assumptions underlying pairwise meta-analysis, that is, the included studies are sufficiently homogenous in terms of the condition being studied, the included participants and the definition of active and control interventions. In other words, we are assuming that the effects of B versus A and C versus B that would have been observed if the C versus A RCT had included all three treatments, is the same as that observed in the B versus A RCT (apart from sample variability). This assumption is the basis for coherent decisions whether they involve two or more treatments. One way to empirically check this is to ask: ’given the known study and participant characteristics, if all these studies compared the same two treatments, would it be suitable to combine them in a meta-analysis?' If the answer to this is yes, and the only distinction is that instead the studies compare different sets of interventions, then the assumption of ‘sufficient homogeneity’ is, in principle, satisfied.Because NMA pools the relative treatment effects estimated across RCTs, within-trial randomisation is preserved. As long as the interventions of interest form a connected network of comparisons, then relative effects of each intervention compared with every other can be obtained, along with estimates of their uncertainty . The first network  includesNMA simultaneously combines the relative treatment effects estimated within each study while accounting for the individual treatments being compared and correctly incorporating studies with more than two arms. Fixed or random effects models can be fitted, the latter allows for between-study heterogeneity. NMA random effects models usually assume that the between-study heterogeneity is the same across all comparisons, that is, a single measure of heterogeneity is calculated across the whole network, although models allowing for different heterogeneity for each comparison can also be fitted.As a statistical model, NMA can be fitted using a frequentist or Bayesian approach.When data are sparse, for example, for adverse or rare events, Bayesian methods have additional advantages such as the ability to better handle studies with zero cells and the potential for including any relevant prior information. However, for most NMAs only simple models are required and no prior information is used, with Bayesian approaches typically defining non-informative prior distributions for all treatment effect parameters, making results from frequentist or Bayesian analyses very similar. The main difference between these approaches is how results are presented. Results from a frequentist NMA will be presented as estimated relative effects  and a 95% CI, whereas results from a Bayesian analysis will be presented as summaries from the posterior distribution of the MD (or OR), which can be the mean or median MD  and their 95% credible interval (CrI).Regardless of the framework used, the fit of the model to the data should be assessed, and in networks with both direct and indirect evidence contributing to estimates, the assumption of consistency should also be statistically assessed. This can be done by comparing the results obtained using the direct evidence alone with the results obtained using the indirect evidence alone,The empty cells in the lower diagonal denote that no direct evidence was available for that comparison , whereas the NMA can make all the comparisons and show that there is evidence of an increase in EGA at delivery for all interventions compared with placebo, except oxytocin receptor blockers .Triangle tables can also be used to report two different outcomes, with one in the top half and the other in the bottom half. This can be a good way to display results from two important outcomes, for example, effectiveness and acceptabilityAn alternative way of reporting ranks is to consider the cumulative probabilities using the surface under the cumulative ranking curve (SUCRA),All probabilities, SUCRA values and rankings should be interpreted with caution as they are very sensitive to the uncertainty in the relative treatment effects used to produce them. Measures or displays which capture this uncertainty such as a table of rank statistics with 95% CrI , rank prImportantly, all results should be interpreted taking into account the uncertainty in the estimates (conveyed by the 95% CrI) as well as the risk of bias in the included evidence. Tools that allow an examination of the impact of studies at risk of bias,When more than two interventions are being considered, synthesis of RCTs using an NMA will ensure that all the relevant evidence, whether direct or indirect, is used to produce coherent estimates of the relative effects of every intervention compared with every other. This allows for more efficient use of the relevant evidence, which can increase the precision of the estimates. In addition, because multiple sources of evidence are used, the final estimates are more robust than if only direct sources of evidence were included, that is, they are less likely to be influenced by the inclusion or exclusion of a single trial. The underlying assumption is that there are no participant or study characteristics that would modify the relative treatment effect of each treatment compared with every other.Relying on multiple pairwise meta-analyses, each including a different set of trials may lead to incoherent decisions and does not make the best use of the available evidence.It is important to display NMA results carefully to aid interpretation and to clinically and statistically assess the plausibility of the assumptions made.


This paper presents a new argument for the Principle of Indifference. This argument can be thought of in two ways: as a pragmatic argument, justifying the principle as needing to hold if one is to minimise worst-case expected loss, or as an epistemic argument, justifying the principle as needing to hold in order to minimise worst-case expected inaccuracy. The question arises as to which interpretation is preferable. I show that the epistemic argument contradicts Evidentialism and suggest that the relative plausibility of Evidentialism provides grounds to prefer the pragmatic interpretation. If this is right, it extends to a general preference for pragmatic arguments for the Principle of Indifference, and also to a general preference for pragmatic arguments for other norms of Bayesian epistemology. As I explain in Section epistemic consequences: if one is to minimise worst-case expected epistemic inaccuracy, then one should satisfy the Principle of Indifference. In Section Many Bayesians are committed to some version or other of the Principle of Indifference, which holds that in certain situations one’s degrees of belief should be equivocal. Section Whilst radical subjectivist Bayesians would want to maintain that there are very few constraints on rational belief and would reject the Principle of Indifference  + P(ω3) = 1} and 𝔼 is convex. In general, however, we shall not assume that the available evidence E consists entirely of propositions that the agent can express, i.e., propositions in the domain Ω of the belief function P. For example, evidence E might alternatively consist of evidence of chances, which, we have assumed, is not expressible in the sense outlined above. If E determines just that the chance function P∗ lies in convex 𝔼, a Bayesian who endorses some form of calibration to chances will want to say that the agent’s belief function P should also lie in E says that P∗ = 0.9, so P∗∈ 𝔼 = {P : P(ω2) + P(ω3) = 0.9}, then arguably also P ∈ 𝔼, which is a convex set of probability functions. If E is inconsistent, we shall take 𝔼 = ℙ, the whole space of probability functions on Ω.Suppose that available evidence PI1: If E = ∅ then P(ω) = 1|Ω|, for each ω ∈Ω.equivocator function and denote it by P=: ω1,ω2,ω3}. The triangle and its interior represent the set of probability functions: each vertex represents the function that gives probability 1 to the corresponding basic possibility and probability 0 to each of the other two; an edge represents the set of probability functions that give probability 0 to the basic possibility at the opposite vertex; all other probability functions are in the interior. If there is no evidence, E = ∅, then 𝔼 is the entire set of probability functions on Ω, and PI1 says that P should be set to the equivocator function, the mid-point of the triangle, which gives probability 13 to each basic possibility.We shall call the probability function that gives the same probability to each basic possibility the mentclass2pt{minimLet us now turn to the first of the three versions of the Principle of Indifference that we shall consider in this paper. This first version says that if there is no evidence at all then one should believe each basic possibility to the same extent: PI2: If E is invariant under permutations of the ω ∈Ω, then P = P=.2This implies PI1, since if E is empty then it is invariant under permutations of the basic possibilities. PI2 is depicted in Fig. ∘ rotations.The second version of the Principle of Indifference that we shall consider says that if the evidence treats each basic possibility symmetrically, then one should believe each such possibility to the same extent: PI3: If P= ∈ 𝔼 then P = P=.This principle, depicted in Fig. The third version of the Principle of Indifference says that if it is compatible with the evidence to believe each basic possibility to the same extent, then one should do so: pragmatic consequences: it turns out that satisfying PI3 is advantageous in that it minimises worst-case expected loss. Later, in Section epistemic consequentialist terms.Having introduced some principles of indifference, in this section I shall develop a consequentialist argument for PI3 which appeals to some technical results of Landes and Williamson . In thisP should be a probability function—and a calibration norm, which says that P should be calibrated to chances. Thus in this section, we shall not presume Probabilism, nor shall we assume that P should be calibrated to chances, as we did in Section evidentially-compatible chance functions: evidence determines that the chance function P∗ lies within set 𝔼 ⊆ ℙ, and 𝔼 is the smallest such set (there is no strict subset 𝔼′⊂ 𝔼 such that the evidence determines that P∗∈ 𝔼′).P within the set P is thus a probability function and is also calibrated to chances insofar as there is evidence of chances. In addition, we shall also see that P should satisfy PI3.The argument presented here does more than justify the Principle of Indifference; it also justifies other Bayesian norms. In particular, it justifies Probabilism—the claim that ω1,ω2,ω3}, and we construe expressible propositions as subsets of Ω, e.g., F = {ω2,ω3}. A partitionπ of propositions is a set of mutually exclusive and exhaustive subsets of Ω. For example, {{ω1},{ω2,ω3}} is a partition of propositions. π will denote the set of all partitions of propositions.In this section we assume, purely for ease of exposition, that bel : Ω→ℝ≥ 0 which attaches a non-negative real number to each expressible proposition. For example, one such belief function might set bel({ω1}) = 6,bel = 9,…. The set of belief functions is a much wider class of functions than the set of probability functions. I shall argue that, in order to minimise worst-case expected loss, the agent’s belief function should be a probability function, in We shall suppose that an agent’s degrees of belief can be represented by a function M = maxπ∈π∑ F∈πbel(F) we normalise belief function bel by considering instead belief function B : Ω→ defined by: bel({ω1}) = 6,bel = 9,bel({ω2}) = 12,bel({ω3}) = 12,… and the partition of propositions {{ω1},{ω2},{ω3}} is given maximum belief, M = 30, then bel would be normalised to give B({ω1}) = 0.2,B = 0.3,B({ω2}) = 0.4,B({ω3}) = 0.4,…. The symbol B will denote the set of all normalised belief functions. For each such function B, B. Note that the set P : Ω→ is a subset of B({ω1}) = 0.2,B = 0.3 then B is not a probability function. The task is to show that whilst in general a belief function B may lie outside the space of probability functions  denote the loss (aka disutility) one should anticipate will be incurred by adopting belief function B when F turns out to be true.specific toF, i.e., the loss in isolation from losses incurred by B on other propositions which may be implied by F or which may imply F. We shall suppose: L1: One should not anticipate any loss when one fully believes a proposition that turns out to be true: L = 0 if B(F) = 1. (Fully believing the truth leads to the best outcome.)L2: One should anticipate that the loss L will strictly increase as B(F) decreases from 1 towards 0. L3: L should depend only on B(F), not on B(F′) for F′≠F.  as the loss incurred on F in isolation from that incurred on other propositions.)L4: Losses should be presumed to be additive when the space Ω of basic possibilities is generated by independent subspaces: whenever ω ∈Ω takes the form ω1 ∧ ω2 where ω1 ∈Ω1 and ω2 ∈Ω2, B(Ω) = 1, and Ω1 and Ω2 are independent in the sense that B(F1 × F2) = B(F1)B(F2) for all 1 and Ω2 respectively.It turns out that these conditions force the loss function to be logarithmic: k  which will be incurred by her degrees of belief. What should she expect of her losses? Let P∗, what is the expected loss incurred by B over all the expressible propositions? A function which measures expected loss is called a scoring rule.g : π→ℝ≥ 0 which attaches a non-negative weight to each partition: Sπ corresponds to the case in which each partition receives weight 1; SΩ corresponds to the case in which each partition of size 2 receives weight 1 and all other partitions get weight 0.7Next, let us consider expected loss. If the chance function is g. First, g is inclusive: every proposition F is in some partition that is given positive weight . Second, g is unbiased: it is invariant under permutations of the basic possibilities ω ∈Ω . The weighting functions for Sπ and SΩ satisfy these two conditions.We shall impose two conditions on Given the anticipated loss function set out above, we have that P∗ will usually not be known, so the expected loss is not fully accessible to the agent. The evidence only determines that P∗ lies in the set B would incur minimum worst-case expected loss? I.e., which B achieves B ∈ B that incurs minimum worst-case expected loss is the probability function in generalised entropy: B ∈ 𝔼 ⊆ ℙ, this establishes that the optimal belief function B is a probability function  and that the optimal belief function B is in the set Now, the precise chance function ℙ that has maximum generalised entropy  = 0 if B(F) = 1. (Fully believing the truth yields no inaccuracy.)E2: Inaccuracy L strictly increases as B(F) decreases from 1 towards 0. E3: Inaccuracy L depends only on B(F), not on B(F′) for F′≠F.  as the inaccuracy of one’s degree of belief in F, in isolation from inaccuracy on other propositions.)E4: Inaccuracy is additive when the space Ω of basic possibilities is generated by independent subspaces: whenever ω ∈Ω takes the form ω1 ∧ ω2 where ω1 ∈Ω1 and ω2 ∈Ω2, B(Ω) = 1, and Ω1 and Ω2 are independent in the sense that P∗(F1 × F2) = P∗(F1)P∗(F2) for all F1 × F2 = {ω1 ∧ ω2 : ω1 ∈ F1,ω2 ∈ F2}, then 1 and Ω2 respectively.The only formal difference here is that E4 appeals to objective independence, i.e., independence with respect to the chance function P∗, rather than subjective independence with respect to the belief function B.The above argument for the Principle of Indifference sought to justify it in terms of its pragmatic consequences: PI3 needs to hold if one is to minimise worst-case expected loss. Interestingly, exactly the same formal argument can be cast in terms of epistemic consequences. The only change involves interpreting the function Everything else follows through as before, but under an epistemic interpretation rather than a pragmatic interpretation. The four conditions set out above force the inaccuracy measure to be logarithmic: We thus appear to be spoilt for choice when justifying the Principle of Indifference. The formal argument for the Principle of Indifference can be given either a pragmatic reading, which appeals to the notion of anticipated loss, or an epistemic reading, which appeals to the notion of inaccuracy. The question arises, should one prefer an epistemic justification over a pragmatic justification or vice versa?Rather than evaluate the details of the particular argument presented above, I will suggest a general answer to this question, in the hope that a general answer will remain pertinent should new pragmatic or epistemic arguments for the Principle of Indifference be put forward in the future. The answer that I will present is that the pragmatic line of argument should be preferred, because of an inconsistency between the epistemic line of argument and Evidentialism.In this section we shall explore the Evidentialism thesis and we shall see that this thesis is inconsistent with the epistemic line of argument presented above.s) given a certain genetic profile (g) is 0.8, i.e., P∗(s|g) = 0.8, and that the chance she has genetic profile g is 0.7, i.e., P∗(g) = 0.7. Regarding survival, these two facts imply that P∗(s) ∈ .s to some degree within the same interval, P(s) ∈ . Now, it is known that a high degree of belief in survival after cancer influences the chance of survival itself  and that belief state is properly believed on the basis of one’s evidence. The stronger sense of rationality is clearly a different sense than that employed in the standard Bayesian notion of rational degree of belief. The Bayesian notion is oblivious to the actual genesis of beliefs, caring only whether the values in question are appropriate in the circumstances.DeRose , §1 notes DeRose  suggestsThere is a further ambiguity that needs to be addressed. Baehr  suggestsIt is important to note that Evidentialism as stated above is simply a biconditional claim, a characterisation of rational belief rather than an analysis of it. It is therefore important to distinguish Evidentialism from an evidentialist epistemology, i.e., a detailed epistemological theory under which Evidentialism turns out to be true. Such an epistemological theory would have as a minimum to give detailed accounts of: rationality; the distinction between epistemic and pragmatic rationality; doxastic deliberation; compatibility; the nature of evidence; and what it is to possess evidence. Work towards an evidentialist epistemology from an internalist point of view can be found in Conee and Feldman , DougherB and a belief function B is compatible with evidence just in case B ∈ E, the set of evidentially-compatible chance functions.Whilst advocates of Evidentialism are not bound to provide a detailed theory of each of the terms that occur in the Evidentialism claim, it is incumbent upon proponents of the claim to clarify when it applies. In particular, it is important to be clear about what a belief state is and when a belief state is compatible with evidence. Given our concern with norms of Bayesian rational degree of belief in this paper, we shall explicate Evidentialism by appealing to the concepts set out in Sections P∗, as far as can be determined by the evidence E. If beliefs should be calibrated to chances then a belief function is compatible with evidence just when it is calibrated to a chance function that is compatible with evidence. This yields the claim that B is compatible with the evidence if and only if B ∈ E. This claim can be motivated more precisely as follows.The motivation behind this explication of compatibility with the evidence appeals to the idea that degrees of belief should be calibrated to chances (a principle common to both the pragmatic approach and the epistemic approach under consideration here). Recall that 𝔼 is defined in Section B∉E then B is incompatible with evidence. Suppose first that E is a set of expressible propositions—propositions that are not statements about chances. Then 𝔼 is the set of all probability functions that give maximal probability to propositions in E, 𝔼 = {P ∈ ℙ : P(𝜃) = 1 for all 𝜃 ∈ E}. Now if B∉E then there is some 𝜃 ∈ E such that B(𝜃) < 1. Such a belief function would be problematic. It would be Moore-paradoxical to recognise that 𝜃 is evidence yet not to fully believe 𝜃. In general, one ought to calibrate one’s beliefs to truths, insofar as one has evidence of them, yet this belief function is not calibrated. Hence, this belief function B is incompatible with evidence. This argument can be extended to the case in which E provides evidence of non-trivial chances. If B∉E then there is some 𝜃 such that B(𝜃)≠P∗(𝜃) for any P∗∈ E. This is also problematic: one ought to calibrate one’s degrees of belief to chances, insofar as one has evidence of them, yet this belief function is not calibrated. Hence, this belief function is incompatible with the evidence.First we shall see that if B ∈ 𝔼 then B is compatible with the evidence, for the following reason. That B ∈ 𝔼 means that as far as can be determined from E, B may well be the true chance function. Suppose new evidence is subsequently obtained that determines that B is indeed the true chance function P∗. This new evidence is clearly compatible with the old evidence E. Call the new evidence base E′ and the new set of evidentially-compatible chance functions E′. Now, B is compatible with E′: E′, we may assume, is consistent, so there must be some belief function compatible with E′; however, as we have just seen, any belief function outside E′; hence, B, the only function in E′, is compatible with E′. Since B is compatible with E′ as a whole, it must be compatible with each item of evidence in E′. Hence it is compatible with each item of evidence in E ⊆ E′, and therefore with E as a whole.On the other hand, if B is compatible with evidence just when B ∈ E. Below, we will revisit the question whether this characterisation is appropriate. In the meantime, we are now in a position to state a Bayesian explication of Evidentialism: BE Belief function B is epistemically rational if and only if B ∈ E.We shall call this specialisation of Evidentialism to degree of belief Bayesian Evidentialism.This provides some motivation for the view that belief function π is 5, even if one has no evidence that this is so. Probabilism, then, seems to be a constraint on rational degree of belief that operates independently of the evidence, contrary to Evidentialism. In response to this concern, it suffices to point out that Probabilism is treated as an idealisation by the Bayesian: Probabilism is usually advocated on the grounds that it is a simple and powerful approximation to a more nuanced, correct norm. In some cases, such as the application of Bayesianism to mathematics  ≥ 0.86 have chance 0.86 of survival, those with degree of belief P(s) ≤ 0.56 have chance 0.56 of survival and those with degree of belief strictly between 0.56 and 0.86 have some chance of survival other than that degree of belief, and that these facts are part of the patient’s evidence. In this case, only the endpoints of the interval, 0.86 and 0.56, are possible values of degrees of belief that are calibrated to the chances. This modified example shows that, in general, some chance functions that are compatible with evidence may not also be interpretable as belief functions compatible with evidence. The set to which belief functions can be calibrated. This qualification avoids a worry about Evidentialism of Reisner  = 1  but P E‡(𝜃) < 1.P E‡ is the only belief function compatible with the evidence. Therefore the belief function that coincides with the truth function ω∗ is deemed to be incompatible with the evidence. This is perverse: despite the fact that the truth is consistent with the evidence, believing the truth is deemed incompatible with the evidence. The original interpretation of ‘compatible with the evidence’ does not suffer from this problem.The problem with this strategy is that the suggested reinterpretation is not a viable interpretation of ‘compatible with the evidence’. This should already be clear, given the motivation for BE provided above. To highlight how wrong the reinterpretation is, suppose that Thus, the proposed reinterpretation is not viable—it strays too far from our usual understanding of ‘compatible’. The conflict between Evidentialism and the epistemic justification of the Principle of Indifference stands.Supervenience.supervene upon one’s evidence.One’s beliefs are epistemically rational if and only if they This epistemic justification is, however, consistent with a significant weakening of the Evidentialism thesis:B ∈ E, and, in addition, B should have maximum generalised entropy. In any case, according to Supervenience, the evidence is only a part of the story.This Supervenience thesis is consistent with the view that compatibility with the evidence is neither sufficient nor necessary for epistemic rationality. Particular implementations of the Supervenience thesis might reinstate necessity, however. One might, for instance, hold that degrees of belief should be compatible with evidence, i.e., the reason for believing no matter what must be found, after sufficient analysis, in data, and in data alone. Russell , p. 401.Despite this, Russell  Some are objections to evidentialist principles other than the Evidentialism thesis as explicated by BE. For example, Sharadin  observesprima facie plausible, the fact that Evidentialism is in tension with the epistemic justification casts some doubt on that justification.We have seen that objections to Evidentialism fail to help the proponent of the epistemic justification of the Principle of Indifference. Since Evidentialism is avoid avoidable loss: in this case, avoid avoidable worst-case expected loss. Hence, the pragmatic reading coheres well with Bayesianism.On the other hand, no such doubt is cast on the pragmatic version of the justification of PI3. This is because, as we saw above, there is no tension between Evidentialism and the pragmatic justification. Furthermore, the pragmatic justification is relatively unproblematic because it rests on the idea that one should avoid avoidable loss, and this goal is central to Bayesianism. The main existing argument for Probabilism—the Dutch book argument  of inaccuracy measure should satisfy the following two requirements. First, there is no other belief function B that has as low inaccuracy as the equivocator function for all basic possibilities: if B≠P= then there is some ω such that I > I. Second, Pettigrew requires that I is invariant under isomorphisms acting on the set of propositions.  The first requirement forces the equivocator function to be less inaccurate than B for some basic possibility, and the second forces the inaccuracy of the equivocator function to be the same for every basic possibility. Hence the equivocator function is the belief function with minimum worst-case inaccuracy, where the worst case is taken over all ω. Thus, Pettigrew argues, PI1 must hold if one is to minimise worst-case inaccuracy.It is worth noting that the epistemic argument of Section It is important to observe that the grounds for preferring the pragmatic justification of Section A further worry is relevant here. Any other epistemic justification of the Principle of Indifference that appeals to inaccuracy will need to provide an account of inaccuracy that is somewhat different to that given in Section Inaccuracy Package: I1: Some specific function f can measure the inaccuracy of a belief function.In the case of our epistemic justification of Section I2: Some unique norm N(f) governs inaccuracy.f. In Pettigrew’s justification, it is to minimise worst-case f. Another norm often invoked by proponents of inaccuracy arguments is to avoid dominated f. Note that these norms conflict; we saw that the first norm warrants PI3 but the second norm does not. Apparently, the third norm, which can be used to motivate Probabilism, cannot be used to justify even PI1 (Pettigrew In the epistemic justification of Section I3: N(f) is a purely epistemic standard.epistemic consideration. Is minimisation of inaccuracy an appropriate standard? Truth is uncontroversially an epistemic standard, but a small improvement in the accuracy of a belief is less obviously so—in many circumstances, a miss is as good as a mile, and one might hold that truth is the only epistemic standard to which beliefs should conform.Inaccuracy needs to be a purely epistemic standard if it is to provide a purely epistemic justification of the Principle of Indifference. Now, the word ‘inaccuracy’ has epistemic connotations, but in the light of the epistemic justification of Section I4: N(f) is necessary for rationality of belief.N(f) in addition? f doesn’t tell us any more about what is true than the evidence does. Of course, we might worry that we’ll lose out in proportion to our inaccuracy—but then avoiding inaccuracy is a pragmatic desideratum, rather than an epistemic one.Truth is an epistemic standard but it is not normally thought of as necessary for rationality of belief. Even if minimisation of inaccuracy is an epistemic standard, some further consideration—currently lacking—needs to be provided before we can be convinced of its necessity for rationality. Furthermore this consideration needs to be clearly epistemic. Suppose our beliefs fit our evidence; should we follow norm I5: N(f) is sufficient for rationality of belief.N(f) can only be sufficient for epistemic rationality if no other norm is necessary. One concern for sufficiency is a slippery slope: if accuracy is necessary for rationality, then why isn’t truth also necessary? The proponent of inaccuracy arguments needs to say why such a move is not warranted. The proponent will want to resist such a move, not only because requiring truth over and above controlling inaccuracy contradicts the sufficiency of N(f), but also because if rational belief is factive then very implausible principles of indifference will follow. For example, suppose that truth were also necessary for rationality of belief in the sense that a degree of belief in basic possibility ω which surpasses some threshold τ, P(ω) > τ, is epistemically rational only when ω is true. Unless the evidence forces the truth of some possibility ω, it is impossible to determine for sure whether ω is true, and hence whether it is epistemically rational to set P(ω) > τ. Hence, the Bayesian can normally only be sure of following this truth norm by setting P(ω) ≤ τ for every ω, i.e., by being sufficiently indifferent between the basic possibilities. In cases where, for some ω, evidence fails to force the truth of ω but implies that the chance of ω exceeds the threshold, P∗(ω) ≥ τ, this truth norm violates the requirement that P ∈ E. Advocating degrees of belief which are incompatible with the evidence clearly goes much further than most Bayesians would like.Now, To get a sense of this complexity, consider that any account of epistemic inaccuracy which underpins a purely epistemic justification of the Principle of Indifference will depend upon the following claims I1–5, which I shall collectively call the n further children, each of whom may or may not come out to play in aminute. They are able to read Emily’s mind, and their algorithm for deciding whether to play outdoors is as follows. If she forms degree of belief 0that there is now achild before her, they will come out to play. If she forms degree of belief 1that there is achild before her, they will roll afair die, and come out to play iff the outcome is an even number. More generally, the summerhouse children will play with chance (1 − 12q(C0)), where q(C0) is the degree of belief Emily adopts in the proposition (C0) that there is now achild before her. Emily’s epistemic decision is the choice of credences in the proposition C0 that there is now achild before her, and, for each j = 1,…,n, the proposition Cj that the j th summerhouse child will be outdoors in afew minutes’ time.Emily is taking awalk through the Garden of Epistemic Imps. Achild plays on the grass in front of her. In anearby summerhouse are The problem is as follows. Assume Emily knows all the facts set out above. Emily’s evidence determines that C0 is true. The Bayesian will want to say that she should fully believe C0. Then, for j = 1,…,n, the chance of each Cj will be 12 and the Bayesian will prescribe degree of belief 12 in each Cj. These latter degrees of belief will be deemed inaccurate by some typical inaccuracy measures f, leading to low overall accuracy. Much greater total accuracy would be achieved if Emily were to fully disbelieve C0, contrary to her evidence, and fully believe C1,…,C10, which would then all be true. Now, if following N(f) were necessary and sufficient for rationality of belief then Emily should adopt the latter beliefs. Such a norm, which requires violating the evidence, would be very unpalatable to the Bayesian.13There is another, more well known, worry about I4–5, presented by Greaves , p. 918:Such examples strengthen the conflict between Evidentialism and inaccuracy arguments. Evidentialism states that compatibility with the evidence is necessary and sufficient for rationality of beliefs. Inaccuracy arguments can conflict both the necessity and sufficiency of this claim: whilst the fact that inaccuracy arguments are used to justify the Principle of Indifference contradicts sufficiency, the Epistemic Imps example tells against necessity.prima facie plausible and objections to Evidentialism miss the mark. Consequently, Evidentialism is more credible than the Inaccuracy Package, and, because of the inconsistency between inaccuracy arguments and Evidentialism, a pragmatic justification of the Principle of Indifference will be on firmer ground than any epistemic justification which appeals to inaccuracy.The Inaccuracy Package, then, is not only complex, it is also riddled with lacunae and challenges. This is not to say that all these challenges are insuperable, rather that, currently, the Inaccuracy Package is merely a promissory note. On the other hand, I have argued that Evidentialism is Let us consider a response to this line of argument, to be found in some comments of Pettigrew , §3.1. Pf when degrees of belief are calibrated to those chances. Third, although the proponent of inaccuracy arguments might claim that the inaccuracy approach is more explanatory, that plays into the hands of the evidentialist, whose objection is precisely that inaccuracy arguments explain too much: they explain pragmatic norms such as the Principle of Indifference when they should only be explaining epistemic norms. Fourth, it is far from clear that the evidentialist fails to explain both the need to gather evidence and the need to fit beliefs to evidence. BE cashes out Evidentialism in terms of calibration of degrees of belief to chances. Better calibration to chances can explain the need to gather more evidence of chances as well as to fit degrees of belief to current evidence of chances.There are various compelling rejoinders open to the evidentialist, however. First, avoiding inaccuracy fails to explain the need to fit evidence. As Pettigrew acknowledges, examples like the Epistemic Imps example set out above show that one should not always fit the evidence if one is to minimise inaccuracy. Second, avoiding inaccuracy also fails to explain the need to gather new evidence. If accuracy were the only goal, one should not gather new evidence wherever that evidence is likely to lead to less accurate degrees of belief—i.e., one should not gather evidence that is likely to reveal that the chances are such as to lead to higher values of the chosen inaccuracy measure Consequently, Pettigrew’s response does not succeed. The inconsistency between Evidentialism and inaccuracy arguments for the Principle of Indifference does indeed favour the former over the latter. A Bayesian seeking a justification for the Principle of Indifference should prefer a pragmatic justification over the epistemic justification of Section We have seen that inaccuracy arguments for the Principle of Indifference are problematic, and that pragmatic arguments arguably fare better in motivating the Principle of Indifference. But these worries extend beyond the Principle of Indifference. This is because inaccuracy arguments for the Principle of Indifference are of exactly the same kind as inaccuracy arguments for other norms of Bayesianism—not only Probabilism but also norms, such as the Principal Principle, which require calibration of degrees of belief to chances  degree of belief would be rational.Second, it appears that this reliability account might conflict with Probabilism, which would be a serious concern for the Bayesian.The third worry about this reliabilist approach is that, although it is unclear whether or not such a line of argument will extend to justifying the Principle of Indifference, difficulties arise either way. On the one hand, so-called ‘concentration theorems’ suggest that probability functions that are indifferent  are overwhelmingly likely (see, e.g., Jaynes Given the current state of play, then, Bayesianism is best motivated pragmatically. Epistemic consequentialism remains an interesting project, but there is a lot more to do before either an approach based on the Inaccuracy Package or a reliabilist approach can offer a viable alternative to pragmatic justifications of Bayesian norms in terms of avoiding avoidable losses.


The labeled cells were injected into the tail veins of rats, and were traced. Neutrophils were rapidly trapped in the liver and the spleen within 6 h. The migration ratios of neutrophils in the lung and the gut were lower compared with those in the liver and the spleen. Interestingly, migrated cells into the spleen were rapidly phagocytosed by monocytes/macrophages. Therefore, accumulation of intact neutrophils in the spleen may be difficult to measure.The systemic migration of neutrophils is not fully understood. In this study, we purified neutrophils from rat peripheral blood and labeled them with [ Specifications TableValue of the data•Neutrophil distribution is important in various inflammatory diseases.•Our data showed rapid neutrophil migration into various organs, and neutrophil clearance in the spleen under normal condition.•These observations are useful for determining neutrophil accumulation and turnover in each organ.151Cr -labeled neutrophils rapidly migrated into the spleen and the liver within 6 h (g for 10 min) (The thin 6 h A. The mithin 6 h B. It was 10 min) C. These 251Cr] sodium chromate  in a CO2 incubator for 1 h at 37 °C. Labeled cells were washed twice and suspended in RPMI 1640 medium containing 10% FBS; 6 × 106 cells were intravenously injected through the tail vein. Following injections, rats were sacrificed by hypovolemic shock with cardiac puncture under anesthesia, and various organs were harvested. The organs or Ficoll-separated blood and spleen fractions were measured by γ-counter   rats , as described in our previous report  Sweden) .Fig. 1Sy


The major advantage of MEG/EEG over other neuroimaging methods is its high temporal resolution. Examining the latency of well-studied components can provide a window into the dynamics of cognitive operations beyond traditional response-time (RT) measurements. While RTs reflect the cumulative duration of all time-consuming cognitive operations involved in a task, component latencies can partition this time into cognitively meaningful sub-steps. Surprisingly, most MEG/EEG studies neglect this advantage and restrict analyses to component amplitudes without considering latencies. The major reasons for this neglect might be that, first, the most easily accessible latency measure (peak latency) is often unreliable and that, second, more complex measures are difficult to conceive, implement, and parametrize. The present article illustrates the key advantages and disadvantages of the three main types of latency-measures , introduces a MATLAB function that extracts all these measures and is compatible with common analysis tools, discusses the most important parameter choices for different research questions and components of interest, and demonstrates its use by various group analyses on one planar gradiometer pair of the publicly available  Remember the last time you picked apples at the grocery store? First, you had to find the shelf with the apples, then decide on the type of apple you want, then to attend to one of the apples and estimate its quality, to store this information in working memory and compare it to the alternative apples in the box; finally you had to program and execute a reaching movement etc. – this is, of course, a ridiculously coarse description of the multitude of cognitive processes involved in picking apples. Beyond any doubt, any cognitive task can be subdivided into a virtually endless number of sub-processes that unfold over time. The goal of cognitive science is to understand these sub-processes and their interplay in detail e.g., . A majormental chronometry  components e.g., . For easpeak latency) – is easily corrupted by neuronal and measurement noise . Finally, onset latency is the time when the component has reached some pre-defined percentage of its amplitude  and its reliability lies somewhere in between percent-area and peak latency. Offset latency can be defined correspondingly as the time where the component has fallen back to the pre-defined percentage, and on- and offset latency will be referred to collectively as percent-amplitude latency. Further latency measures that were developed for specific components are not treated here . According to this definition, area is confined by the time window, the x-axis  and the ERP/ERF. To avoid missing some of the activity in some components of some individuals one would have to pick a rather broad window. This, however, incurs the risk of including activity from adjacent components or noise into the calculation and thus – depending on the data – to introduce a bias to the estimate  might introduce artifacts and often not only decrease the noise level, but also the signal , any latency estimate is much less influenced by noise. Still the variance between the leave-one-out averages provides some indication of the error variance  and (appropriately adjusted) statistical tests can be performed , so that they can be submitted to statistical tests.3 This function can be used with output from common MATLAB-based analysis toolboxes . All code needed to generate raw versions of the figures shown above and the example analyses described below from the Wakeman-and-Henson data is deposited at figshare5. Note that the purpose of this article is not to introduce or validate any new method, but to provide a tutorial example of how to extract latency estimates for running group analyses from already preprocessed data, including statistical tests of latency differences between conditions.The following, will demonstrate how all these latency estimates (and versions thereof) are extracted from group data, using the MATLAB  function  EEGlab,  or data latency.m. A list of all parameters with a short description, including the default values, as well as all possible outputs is contained in the MATLAB function and displayed using help latency. The function expects two input arguments: the individual, preprocessed averages (avgs) and a configuration structure6 . The input avgs is a Subjects × Channels × Time matrix with the individual averages or a structure that contains such a matrix as the fields ‘data’ (EEGlab) or ‘individual’ (Fieldtrip); one can also specify the name of the data field via cfg.datafield. The function also recognizes a cell array of structures (one structure for each subject) with the data stored in the field ‘avg’ (such as those produced by the Oostenveld script). The only parameter that must be specified is the sign of the component by setting cfg.sign to either ‘1’ or ‘-1’ . One should additionally indicate which type of latency estimate to extract, for example, cfg.extract = ‘peakLat’. With these settings, latency.m returns the latency of the local maximum in the indicated direction (peak latency) with data averaged across all channels. Usually, one would like to restrict the temporal search space by setting cfg.peakWin according to the temporal extent of the component . Channels of interest are selected via cfg.chans. Indicate either multiple channels if an unweighted average across these channels is desired  or only one channel . To include only specific subjects, select them via cfg.subs. Time points, channels, and subjects are by default addressed by their positions in the matrix (indices); cfg.peakWin expects start and end points, cfg.chans and cfg.subs expect all indices . Alternatively, subjects can be addressed by their designation in the experiment if, additionally, a list of subject numbers is provided as cfg.subNum (array of integers). The same is possible for channels by specifying cfg.chanNames (cell array of strings) and times by specifying cfg.times (array of numbers). Notably, if cfg.times is specified, all parameters specifying times are interpreted as and latency estimate(s) are returned in the units of cfg.times (ms or s) instead of sampling points. cfg.subNum, cfg.chanNames and cfg.times must be the same order as in the Subjects × Channels × Time data matrix.The following describes the various input parameters  latency measure(s) a priori by setting cfg.extract. Use tilted brackets to submit a list of desired output measures, separated by commas. Possible latency measures are peak latency (‘peakLat’), percent-amplitude latency before (‘onset’) or after (‘offset’) the peak, and percent-area latency (‘areaLat’). In addition it is possible to extract other measures that are created along the way or might be useful for other analyses or quality checks, namely mean amplitude (‘mean’), peak amplitude (‘peakAmp’), total area under the curve (‘area’), width of the component , difference in peak amplitude between the component of interest and an adjacent component of opposite polarity (‘peak2peak’), and the new baseline for the area boundary (‘baseline’). ‘counterAmp’ and ‘counterLat’ are the amplitude and the latency of the preceding (cfg.cWinWidth < 0) or following (cfg.cWinWidth > 0) adjacent peak (referred to as counter peak here). Furthermore, there are several Booleans that indicate for each subject whether a local peak was found 7, whether on- and offsets were found  and whether a point dividing the area into the desired percentage (area latency) was found (‘foundArea’). When a single output measure is requested, the output res is a vector with one value for each participant, otherwise res is a structure with the respective fields for each output measure. An additional output can be requested (cfgNew), which contains all information on the final settings, including parameters that were not set at call and were filled with default values, and the field ann, which contains some in depth information on the extracted measures that might be useful for understanding and reporting the results.If peakLat) is extracted from the desired spatiotemporal analysis window without any additional parameters. Percent-amplitude latency (onset and offset) and percent-area latency (areaLat) require some parametrizing, which will be detailed right away.Peak latency  should be an average across several sampling points to avoid contamination by high-frequency noise. Thus, a second parameter that determines the width of this averaging window is necessary. cfg.peakWidth is the number of sampling points to the left and right of the peak latency that is averaged and defaults to 5 sampling points or ∼5 ms . The same number of sampling points is also averaged for determining the amplitude at on- and offset (which is used for the decision whether the desired percentage of the amplitude has been reached), so that percent-amplitude latency is not contaminated by high-frequency noise . This percentage is set via latency.m by setting cfg.cWinWidth. This parameter indicates how much before  or after  the search-window border  or the peak of the component of interest (cfg.cWinStart = ‘peak’) the algorithm should look for the peak of the adjacent component. If cfg.cWinWidth is set, cfg.percAmp no longer refers to the percentage of component amplitude relative to the pre-stimulus baseline, but to the percentage of the peak-to-peak amplitude .As discussed above  does not restrict the search range. If cfg.cWinWidth is set and cfg.cBound is true (default), the peak of the other component determines one temporal boundary. Alternatively, cfg.ampLatBound can be set by hand by providing start and end times.cfg.percArea; typically 50%). As discussed above, the crux is the definition of area. In the simplest (default) case, the area is confined in amplitude space by the ERP/ERF and the pre-stimulus baseline (cfg.areaBase = ‘zero’). If cfg.areaBase is set to ‘percAmp’ the desired percentage of the peak amplitude (cfg.percAmp) serves as a boundary in amplitude space; in a way, the baseline is moved toward the peak of the component  or by the on- and offsets (cfg.areaWin = ‘ampLat’).Percent-area latency is the time point where a component has reached a certain percentage of its area  contains a small function called jackT.m implementing the cfg.aggregation = ‘jackSmulders.’ If latencies of the grand average are needed, set cfg.aggregation = ‘GA.’The default output of al tests . For paicfg.fig = true), and (c) the descriptions of the various latency measures above and the further advice and the examples below point to quite a few principles that can be used to considerably restrict the parameter space a priori. Furthermore, using on- and offsets to constrain the area  before the peak detection is typically necessary see also . Percentcfg.cWinStart and cfg.cWinWidth) heavily depends on the observed data pattern and can likely not be fully determined a priori, but must be based on an inspection of the grand average across conditions (see previous section). Baseline adaptation and adjacent-peak anchor are typically unnecessary if the component of interest is well isolated during preprocessing .If the component of interest occurs relatively late and there is contamination with low-frequency noise or slow-wave activity, the baseline adaptation of cfg.sign according to the direction of the component, run  = latency, read the warning messages and inspect the contents of cfgNew, which will tell you about the default parameters. This should already provide quite some clues on which parameters one would like to change. Warnings can be turned off by setting cfg.warnings = false.A good way to get used to the function is to set cfg.peakWin) instead. If this happens too often , the algorithm returns a warning. This typically indicates that the search interval is set too narrowly and, thus, increasing cfg.peakWin often helps. The output res.foundLocal indicates for each subject whether a local peak was found or not and, thus, helps to identify anomalous individual averages .One common problem is that no local peak is found and the algorithm returns one of the boundaries of the search interval (cfg.ampLatWin). If this happens too often , the algorithm returns a warning. One reason might be that a slow-wave component overlays the component of interest. In this case, using the adjacent peak as an anchor point and changing the baseline as described above holds promise to considerably improve the results. If this occurs for only a few participants, their individual averages might be too noisy and should be rejected. Inspect ‘res.foundOn’ and ‘res.foundOff’ to identify such corrupted data sets.Percent-amplitude latency is corrupted when the ERP/ERF does not cross the baseline before or after the peak. This is the case when the indicated percentage of the component amplitude is already reached before the onset of the search interval or activity does not fall sufficiently again after the component’s peak. The algorithm than sets the on- or offset estimate to the respective search boundary , which are defined as 30% of the peak amplitude (cfg.percAmp = 0.3) relative to the adjacent negative peak. With regard to the shape of the overall ERF displayed in Figure cfg.peakWin = ) and 400–1,000 ms  for the peak of the early and late component, respectively. The adjacent peak is searched starting 200 ms before the search window for the late component (cfg.cWinWidth = -0.2) and until 100 ms after the search window for the early component (cfg.cWinWidth = 0.1).As a practical example, the usage of t(15) = 3.39, p = 0.004, but not between famous and unfamiliar faces, t(15) = 1.24, p = 0.234, t(15) = 1.20, p = 0.249, t(15) = 0.50, p = 0.627, t(15) = 3.51, p = 0.003, t(15) = 0.15, p = 0.885, t(15) = 1.58, p = 0.135, t(15) = 0.35, p = 0.734, Indeed, confirming the initial observation, the 50%-area latency of the late component differed between intact and scrambled faces  = 1.06, p = 0.306, t(15) = 0.01, p = 0.991, t(15) = 0.94, p = 0.364, t(15) = 0.06, p = 0.956, t(15) = 3.43, p = 0.004, t(15) = 0.55, p = 0.591, t(15) = 3.36, p = 0.004, t(15) = 0.48, p = 0.639, Surprisingly, jackknife analyses of the late component’s peak latency did not confirm the pattern, r = 0.94, p < 0.001, second for onset latency, r = 0.86, p < 0.001, and worst  for peak latency, r = 0.70, p = 0.002. Furthermore, the inter-correlations of the various measures of the late component’s latency might serve to gauge in how far these measures capture the same aspects of the underlying process. Area latency correlated highly with peak latency, r = 0.84 , p < 0.001, and weaker with onset latency, r = 0.50 (rcorr = 0.56), p = 0.048, but the correlation between onset and peak latency did not reach significance, r = 0.21 (rcorr = 0.27), p = 0.436. As makes intuitive sense, area latency and peak latency pick up the same variance, which is different though somewhat related to onset latency.These analyses indicate that latency does not differ depending on whether the face is famous or unfamiliar. This opens the interesting possibility to use the correlation between the late component’s latencies for famous and unfamiliar faces as an index of the reliability of the various measures. This estimate of reliability was highest for area latency, latency.m) and applied this function to an MEG data set (latency.m used here, a function for performing paired t-tests on jackknife data, and a MATLAB script and all dependencies for reproducing raw versions of all figures as well as all results of the example analyses. The most current version of latency.m can be downloaded from https://github.com/Liesefeld/latency.The present article illustrated the importance of component-latency measures for cognitive theories, introduced the three most common latency measures and variants thereof and discussed their strength and weaknesses. Furthermore, it described a function that can extract all these measures from group data (data set . An accoHRL is the sole author and has done all the work involved in preparing and writing this manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Prostate cancer is still the most frequently diagnosed cancer in American men despite a 7.6% decrease in cancer incidence between 2011 and 2014  or Ultrahypofractionation radiotherapy, also referred to “extreme hypofractionation”, “stereotactic ablative body radiation therapy” and “stereotactic body radiation therapy” (SBRT) for Localized Prostate Cancer (PCa) from the ASTRO, ASCO, and AUA were published -5.This consensus was based on key questions that addressed the indications of the different schedules of hypofractionation for the different risk groups of PCa when compared to conventionally fractioned external beam radiotherapy (EBRT), using daily fractions of 180-200 cGy given in 7 to 8 weeks, for doses up to 7,800 cGy.Several studies have already provided evidence for the efficacy of dose-escalation on biochemical control (BC) of PCa and results from randomized trials (RCTs) have shown a direct relation between increasing the radiation dose given to the prostate and/or seminal vesicles and BC -9; howevUnfortunately the consensus on the use of hRT did not include forms of hypofractionation that combine different techniques of radiation like EBRT associated to brachytherapy, using either high or low dose rate sources. The role of high dose rate (HDR) brachytherapy in the treatment of men with PCa is not well defined, but the results of the trials mentioned above have shown that escalated doses are superior to conventional doses to achieve BC in all risk groups of PCa. HDR brachyteharpy can escalate the dose given to the prostate by the combination with EBRT and has also the potential biological advantage through the delivery of doses in higher levels than the ones evaluated in the published consensus . Mature Moderate hRT was defined in that guideline as treatments given with fractions size between 240 cGy and 340 cGy per day, three to five times a week over 3.8 to 5.6 weeks. SBRT was defined as EBRT administered with fractions size of more than 500 cGy independent of considerations of technique used.The literature has four large prospective RCTs and additional single institution RCTs demonstrating that hRT provides BC that is similar to EBRT. It is also important to point out that, despite a limited follow-up beyond five years for most RCTs, a small increased risk of acute gastrointestinal toxicity is observed, but with similar late gastrointestinal risk. No additional acute and late genitourinary toxicity with hRT was noted -23.It is important to highlight the fact that the optimal radiation regimen still cannot be determined since most of the multiple fractionation schemes evaluated in clinical trials have not been compared in parallel. Regimens of 6,000 cGy and 7,000 cGy, given in 20 and 28 daily fractions, respectively are the most frequent found in the literature.Information regarding dose constraints is also conflicting, but three trials published reported constraints for bladder and rectum on solid contours , 24, 25.To date, there are no published efficacy and toxicity data from RCTs comparing SBRT and conventionally fractionated EBRT, nor specific normal tissue constraints. Most of published results apply to patients with prostate volumes up to 100 cm3 and with mild to moderate urinary symptoms at baseline. Doses of 3,500 to 3,625 cGy given in 5 fractions of 700 to 725 cGy are recommended, and on plan evaluations at least two dose-volume constraint points for rectum and bladder shall be used , 27.The quality of evidence of each recommendation statement was categorized on the grade guidelines published by Balshem et al.  as high,“High: the panel was very confident that the true effect lies close to that of the estimate of the effect;Moderate: the panel was moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different;Low: the panel confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect;Very Low: the panel has very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate.”The following questions were addressed;Key Question 1 – hRT is indicated for the following risk groups of PCa.1A - For low-risk prostate.Recommendation strength: StrongQuality of evidence: HighConsensus: 100%1B - For intermediate-risk prostate.Recommendation strength: StrongQuality of evidence: HighConsensus: 100%1C - For high-risk prostate (not receiving pelvic lymph nodes irradiation).Recommendation strength: StrongQuality of evidence: HighConsensus: 94%Key Question 2 – Men should be counseled about the small increased risk of acute gastrointestinal (GI) toxicity with hRT?Recommendation strength: StrongQuality of evidence: HighConsensus: 100%Key Question 3 – Regarding patient age, associated comorbidity, anatomy, or urinary function and regimens of 60 Gy and 70 Gy, given in 20 and 28 daily fractions.3A – The optimal regimen cannot be determined.Recommendation strength: ConditionalQuality of evidence: ModerateConsensus: 100%3B – one moderately hypofractionated regimen is not suggested over another and hRT regimens do not appear to be impacted by patient age, comorbidity, anatomy, or urinary function.Recommendation strength: ConditionalQuality of evidence: ModerateConsensus: 100%Key Question 4 – SBRT and risk groups of PCa.4A - for low-risk prostate SBRT may be offered as an alternative to conventional fractionation.Recommendation strength: ConditionalQuality of evidence: ModerateConsensus: 88%4B - for intermediate-risk prostate SBRT may be offered as an alternative to conventional fractionation.Strength of recommendation: ConditionalQuality of evidence: LowConsensus: 94%4C - for high-risk prostate SBRT may be offered as an alternative to conventional fractionation.Strength of recommendation: ConditionalQuality of evidence: LowConsensus: 94%3.Key Question 5 - SBRT may be offered to low- and intermediate-risk patients with prostate sizes less than 100 cmRecommendation strength: ConditionalQuality of evidence: ModerateConsensus: 88%Key Question 6 - regarding normal tissues constraints.Statement: At least two dose-volume constraint points for rectum and bladder should be used for hRT or SBRT: one at the high-dose end  and one in the mid-dose range .Recommendation strength: StrongQuality of evidence: ModerateConsensus: 100%Key Question 6 – the associated margin definitions for the target.Most commonly reported margins describe an isotropic 5 mm expansion around the CTV with the exception of a 3 mm posterior expansion. So, it is not recommended to use margins that deviate from those already published and used as references in the consensus.Recommendation strength: StrongQuality of evidence: LowConsensus: 100%Key Question 7 – Image guided radiotherapy (IGRT) should be universally recommended when delivering hRT or SBRT.Recommendation strength: StrongQuality of evidence: ModerateConsensus: 100%Key Question 8 – Non-modulated techniques are not recommended when delivering hRT or SBRT.Recommendation strength: StrongQuality of evidence: ModerateConsensus: 100%