Long-term ozone exposure associated cause-specific mortality risks with adjusted metrics by cohort studies: A systematic review and meta-analysis BACKGROUNDLong-term ozone (O3) exposure could lead to a series of non-communicable diseases and increase the mortality risks. However, cohort-based studies were still rather rare, and inconsistent exposure metrics might impair the credibility of epidemiological evidence synthetisation. To provide more accurate meta-estimation, this review updated the systematic review with inclusion of recent studies and summarised the quantitative associations between O3 exposure and cause-specific mortality risks based on unified exposure metrics.

METHODSResearch articles reporting relative risks between incremental long-term O3 exposure and causes of mortality covering all-cause, cardiovascular diseases, respiratory diseases, chronic obstructive pulmonary disease, pneumonia, ischaemic heart diseases, ischaemic stroke, congestive heart failure, cerebrovascular diseases, and lung cancer, estimated from cohort studies were identified through systematic searches in MEDLINE, Embase and Web of Science. Cross-metric conversion factors were estimated linearly by decadal of observations during 1990-2019. The Hunter-Schmidt random effect estimator was applied to pool the relative risks.

RESULTSA total of 25 studies involving 226,453,067 participants (14 unique cohorts covering 99,855,611 participants) were included in the systematic review. After linearly adjusting the inconsistent O3 exposure metrics into congruity, the pooled relative risks (RR) associated with every 10 nmol mol-1 (ppbV) incremental O3 exposure, by mean of warm-season daily maximum 8-hour average metric, was: 1.014 with 95% confidence interval (CI) ranging 1.009-1.019 for all-cause mortality; 1.025 (95% CI: 1.010-1.040) for respiratory mortality; 1.056 (95% CI: 1.029-1.084) for COPD mortality; 1.019 (95% CI: 1.004-1.035) for cardiovascular mortality; and 1.096 (95% CI: 1.065-1.129) for congestive heart failure mortality. Insignificant mortality risk associations were found for ischaemic heart disease, cerebrovascular diseases and lung cancer.

DISCUSSIONThis review covered up-to-date studies, expanded the O3-exposure associated mortality causes into wider range of categories, and firstly highlighted the issue of inconsistency in O3 exposure metrics. Non-intercept linear regression-based cross-metric RR conversion was another innovation, but limitation lay in the observation reliance, indicating further calibration with more credible observations available. Large uncertainties in the multi-study pooled RRs would inspire more future studies to corroborate or contradict the results from this review.

CONCLUSIONAdjustment for exposure metrics laid more solid foundation for multi-study meta-analysis, and wider coverage of surface O3 observations are anticipated to strengthen the cross-metric conversion in the future. Ever-growing numbers of epidemiological studies supported unneglectable cardiopulmonary hazards and all-cause mortality risks from long-term O3 exposure. However, evidences on long-term O3 exposure associated health effects were still scarce, and hence more relevant studies are encouraged to cover more population with regional diversity.

REGISTRATIONThe review was registered in PROSPERO (CRD42021270637).

FUNDINGThis study is mainly funded by UK Natural Environment Research Council, UK National Centre for Atmospheric Science, Australian Research Council and Australian National Health and Medical Research Council.

HighlightsO_LIUpdated evidence for O3-mortality associations from 25 cohorts has been provided.
C_LIO_LIAdjusting various O3 exposure metrics can provide more accurate risk estimations.
C_LIO_LILong-term O3-exposure was associated with increased mortality from all-causes, respiratory disease, COPD, cardiovascular disease and congestive heart failure.
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Time trends, factors associated with, and reasons for COVID-19 vaccine hesitancy in US adults: January-May 2021 ImportanceCOVID-19 vaccine hesitancy has become a leading barrier to increasing the US vaccination rate.

ObjectiveTo evaluate time trends in COVID-19 vaccine intent during the US vaccine rollout, and identify key factors related to and self-reported reasons for COVID-19 vaccine hesitancy in May 2021.

Design, participants and settingA COVID-19 survey was offered to US adult Facebook users in several languages yielding 5,088,772 qualifying responses from January 6 to May 31, 2021. Data was aggregated by month. Survey weights matched the sample to the age, gender, and state profile of the US population.

ExposureDemographics, geographic factors, political/COVID-19 environment, health status, beliefs, and behaviors.

Main outcome measures"If a vaccine to prevent COVID-19 were offered to you today, would you choose to get vaccinated." Hesitant was defined as responding probably or definitely would not choose to get vaccinated (versus probably or definitely would, or already vaccinated).

ResultsCOVID-19 vaccine hesitancy decreased by one-third from 25.4% (95%CI, 25.3, 25.5) in January to 16.6% (95% CI, 16.4, 16.7) in May, with relatively large decreases among participants with Black, Pacific Islander or Hispanic race/ethnicity and [&le;]high school education. Independent risk factors for vaccine hesitancy in May (N=525,644) included younger age, non-Asian race, < 4 year college degree, living in a more rural county, living in a county with higher Trump vote share in the 2020 election, lack of worry about COVID-19, working outside the home, never intentionally avoiding contact with others, and no past-year flu vaccine. Differences in hesitancy by race/ethnicity varied by age (e.g., Black adults more hesitant than White adults <35 years old, but less hesitant among adults [&ge;]45 years old). Differences in hesitancy by age varied by race/ethnicity. Almost half of vaccine hesitant respondents reported fear of side effects (49.2% [95%CI, 48.7, 49.7]) and not trusting the COVID-19 vaccine (48.4% [95%CI, 48.0, 48.9]); over one-third reported not trusting the government, not needing the vaccine, and waiting to see if safe. Reasons differed by degree of vaccine intent and by race/ethnicity.

ConclusionCOVID-19 vaccine hesitancy varied by demographics, geography, beliefs, and behaviors, indicating a need for a range of messaging and policy options to target high-hesitancy groups.
Epicardial adipose tissue thickness is associated with increased COVID-19 severity and mortality BACKGROUNDIncreased adiposity and visceral obesity have been linked to adverse COVID-19 outcomes. The amount of epicardial adipose tissue (EAT) may have relevant implications given its proximity to the heart and lungs. Here, we explored the role of EAT in increasing the risk for COVID-19 adverse outcomes.

METHODSWe included 748 patients with COVID-19 attending a reference center in Mexico City. EAT thickness, sub-thoracic and extra-pericardial fat were measured using thoracic CT scans. We explored the association of each thoracic adipose tissue compartment with COVID-19 mortality and severe COVID-19 (defined as mortality and need for invasive mechanical ventilation), according to the presence or absence of obesity. Mediation analyses evaluated the role of EAT in facilitating the effect of age, body mass index and cardiac troponin levels with COVID-19 outcomes.

RESULTSEAT thickness was associated with increased risk of COVID-19 mortality (HR 1.18, 95%CI 1.01-1.39) independent of age, gender, comorbid conditions and BMI. Increased EAT was associated with lower SpO2 and PaFi index and higher levels of cardiac troponins, D-dimer, fibrinogen, C-reactive protein, and 4C severity score, independent of obesity. EAT mediated 13.1% (95%CI 3.67-28.0%) and 5.1% (95%CI 0.19-14.0%) of the effect of age and 19.4% (95%CI 4.67-63.0%) and 12.8% (95%CI 0.03-46.0%) of the effect of BMI on requirement for intubation and mortality, respectively. EAT also mediated the effect of increased cardiac troponins on myocardial infarction during COVID-19.

CONCLUSIONEAT is an independent risk factor for severe COVID-19 and mortality independent of obesity. EAT partly mediates the effect of age and BMI and increased cardiac troponins on adverse COVID-19 outcomes. Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis BackgroundElectroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers.

ObjectiveTo develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting.

MethodsMultimodal data (i.e., clinical, sMRI and resting-state fMRI) was collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluated which data modalities or combinations thereof could provide the best predictions for treatment response ([&ge;]50% symptom reduction) or remission (HAM-D score [&le;]7) using a support vector machine classifier.

ResultsRemission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers, and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC).

ConclusionsThese results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible. The Association of Opening K-12 Schools with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis AO_SCPLOWBSTRACTC_SCPLOWThis paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States. Using data on foot traffic and K-12 school opening plans, we analyze how an increase in visits to schools and opening schools with different teaching methods (in-person, hybrid, and remote) is related to the 2-weeks forward growth rate of confirmed COVID-19 cases. Our debiased panel data regression analysis with a set of county dummies, interactions of state and week dummies, and other controls shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the positive association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These results have a causal interpretation in a structural model with unobserved county and time confounders. Sensitivity analysis shows that the baseline results are robust to timing assumptions and alternative specifications. Using explainable machine learning to identifypatients at risk of reattendance at discharge from emergency departments Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosted decision tree model trained to identify patients at risk of reattendance within 72 hours of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8,847 attendances by 7,237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI : 0.722-0.773) and an average precision of 0.233 (95% CI : 0.194-0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques can be used to investigate the different sub-groups of explanations present in our patient cohort. C-reactive protein and lactate dehydrogenase as prognostic indicators in COVID-2019 outpatients BackgroundIt is critical for clinicians seeing outpatients with coronavirus disease 2019 (COVID-19) to identify those who will require oxygen therapy after the hospital visit. Although studies on biomarkers predicting mortality or ventilator requirement in hospitalized patients with COVID-19 have been conducted, research on biomarkers predicting the need for oxygen therapy in outpatients is sparse.

MethodsPatients with COVID-19 who visited Asahikawa City Hospital on an outpatient basis were included in the study. In total, 287 new outpatients visited between April 2021 and September 2021, and 142 underwent blood testing. All blood tests were performed before any treatments for COVID-19 were started. Demographic information, laboratory data, and clinical treatment information were extracted from the electronic medical records. Risk factors associated with oxygen therapy were explored.

ResultsIn total, 40 of 142 patients who underwent blood testing required oxygen therapy within 7 days after blood samples were taken, and all other patients recovered without oxygen therapy. C-reactive protein (CRP) and lactate dehydrogenase (LDH) levels were significantly higher in patients who required oxygen therapy, and their cutoffs were 36 mg/L (sensitivity, 0.802; specificity, 0.725) and 267 U/L (sensitivity, 0.713; specificity, 0.750), respectively. Multivariate logistic regression identified age, body mass index, CRP [&ge;] 36 mg/L, and LDH [&ge;] 267 U/L as significant risk factors for oxygen therapy requirement. This study suggests that elevated CRP and LDH levels are independent risk factors for oxygen therapy in outpatients with COVID-19. Further confirmatory studies are needed. Macular vascular complexity analysis of diabetes mellitus by swept-source optical coherence tomographic angiography PurposeTo evaluate the associations of retinal vascular complexity features, including fractal dimension (FD) and blood vessel tortuosity (BVT), with the severity of diabetic retinopathy (DR) by using noninvasive optical coherence tomographic angiography (OCTA).

MethodsThis prospective cross-sectional study recruited ocular-treatment-naive patients with type 2 diabetes mellitus (DM) registered in the community of Guangzhou, China. OCTA was used to obtain the measurements of FD and BVT in the superficial capillary plexus (SCP) and the deep capillary plexus (DCP). Univariate and multivariate linear regression was used to analyse the correlation of FD and BVT in different layers with DR severity.

ResultsA total of 1282 patients with DM (1282 eyes), with a mean age of 64.2{+/-}7.8 years, were included. FD in the DCP decreased and BVT in the DCP increased in DR patients compared to non-DR patients, even after adjusting for confounding factors (P< 0.05). Trend analysis showed a significant decrease in the FD values as the DR progressed, while the BVT progressively increased with worsening DR severity (P< 0.01). The FD in DCP had a statistically significant positive correlation with FD in SCP and a negative correlation with BVT in SCP and BVT in DCP in all of the participants, including the non-DR group, moderate DR group and severe DR group (P< 0.01).

ConclusionFD and BVT determined using OCTA might be useful parameters for objectively distinguishing DR from non-DR and indicating DR progression. Children develop strong and sustained cross-reactive immune responses against Spike protein following SARS-CoV-2 infection, with enhanced recognition of variants of concern SARS-CoV-2 infection is generally mild or asymptomatic in children but the biological basis for this is unclear. We studied the profile of antibody and cellular immunity in children aged 3-11 years in comparison with adults. Antibody responses against spike and receptor binding domain (RBD) were high in children and seroconversion boosted antibody responses against seasonal Beta-coronaviruses through cross-recognition of the S2 domain. Seroneutralisation assays against alpha, beta and delta SARS-CoV-2 variants demonstrated comparable neutralising activity between children and adults. T cell responses against spike were >2-fold higher in children compared to adults and displayed a TH1 cytokine profile. SARS-CoV-2 spike-specific T cells were also detected in many seronegative children, revealing pre-existing responses that were cross-reactive with seasonal Alpha and Beta-coronaviruses. Importantly, all children retained high antibody titres and cellular responses at 6 months after infection whilst relative antibody waning was seen in adults. Spike-specific responses in children also remained broadly stable beyond 12 months. Children thus distinctly generate robust, cross-reactive and sustained immune responses after SARS-CoV-2 infection with focussed specificity against spike protein. These observations demonstrate novel features of SARS-CoV-2-specific immune responses in children and may provide insight into their relative clinical protection. Furthermore, this information will help to guide the introduction of vaccination regimens in the paediatric population. The incidence of delirium in an acute geriatric community hospital: an observational cohort feasibility study. ObjectivesDelirium in hospitalised older adults is associated with negative health outcomes. Admission to an alternative care setting may lower the incidence of delirium. The Acute Geriatric Community Hospital (AGCH) was recently opened in the Netherlands and uses a multi-component non-pharmacological intervention strategy to prevent delirium. The objective of this study was to describe the incidence of delirium at the AGCH and compare this incidence to existing rates from literature. If a possible effect on delirium is seen in this comparison this would support conducting a larger prospectively controlled study on delirium in this new care setting.

DesignProspective cohort feasibility study; exploratory meta-analysis of proportions.

Setting and ParticipantsThe AGCH is an acute geriatric unit in a skilled nursing facility for patients aged >65 years with acute medical conditions.

MethodsDelirium assessment using the Confusion Assessment Method (CAM) upon admission and on day one, two and three or until delirium had resolved. Patients charts were reviewed if CAM was missing. In an linear mixed-effects model, the delirium incidence rate in AGCH was compared to pooled delirium incidence rates from six studies found in a high-quality review.

Results214 patients from the AGCH (mean age 81.9 years, 47% male, 12% with a history of dementia) were included in the analysis. Delirium developed in 8% (18/214) (95% confidence interval [CI] 5-13%) of patients during AGCH admission compared to 16% (95% CI 12-21%) in hospitals. Admission to the AGCH was associated with a decreased delirium incidence rate compared to the hospital control group (OR[odds ratio]= 0.49, 95% CI 0.24-0.98, p-value=0.044).

Conclusions and implicationsThe delirium incidence in the AGCH was low compared to those incidences found in general hospitals in literature. Based on these findings a controlled observational or randomized study measuring delirium in this care setting is recommended. Human lipoproteins comprise at least 12 different classes that are lognormally distributed. Lipoproteins in medical samples have been measured by enzymatic methods that coincide with conventional ultracentrifugation. However, the high gravity and time required for ultracentrifugation can cause sample degradation. This study presents the results of HPLC, a gentler and rapid separation method, for 55 human serum samples. The elution patterns were analysed parametrically, and the attribute of each class was confirmed biochemically. Human samples contained 12 classes of lipoproteins, each of which may consist primarily of proteins. There are three classes of VLDLs. The level of each class was distributed lognormally, and the standard amount and the 95% range were estimated. Enzymatic methods measure the levels of several mixed classes. This lognormal character suggests that the levels are controlled by the synergy of multiple factors. Multi-Analyte Liquid Biopsies for Molecular Pathway Guided Personalized Treatment Selection in Advanced Refractory Cancers: A Clinical Utility Pilot Study PurposeThe selection of safe and efficacious anticancer regimens for treatment of broadly refractory patients with metastatic cancers remains a clinical challenge. Such patients are often fatigued by toxicities of prior failed treatments and may have no further viable standard of care treatment options. Liquid Biopsy-based multi-analyte profiling in peripheral blood can identify a majority of drug targets that can guide the selection of efficacious combination regimens.

Patients and MethodsLIQUID IMPACT was a pilot clinical study where patients with advanced refractory cancers received combination anticancer treatment regimens based on multi-analyte liquid biopsy (MLB) profiling of circulating tumor biomarkers; this study design was based on the findings of prior feasibility analysis to determine the abundance of targetable variants in blood specimens from 1299 real-world cases of advanced refractory cancers.

ResultsAmong the 29 patients in the intent to treat (ITT) cohort of the trial, 26 were finally evaluable as per study criteria out of whom 12 patients showed Partial Response (PR) indicating an Objective Response Rate (ORR) of 46.2% and 11 patients showed Stable Disease (SD) indicating the Disease Control Rate (DCR) to be 88.5%. The median Progression-Free Survival (mPFS) and median Overall Survival (mOS) were 4.3 months (95% CI: 3.0 - 5.6 months) and 8.8 months (95% CI: 7.0 - 10.7 months), respectively. Toxicities were manageable and there were no treatment-related deaths.

ConclusionThe study findings suggest that MLB could be used to assist treatment selection in heavily pretreated patients with advanced refractory cancers. The effects of modifiable maternal pregnancy risk factors on molar-incisor hypomineralisation: A negative control study ObjectivesO_LITo explore the associations between modifiable maternal pregnancy exposures: pre-pregnancy body mass index (BMI), pregnancy smoking and alcohol consumption with offspring molar-incisor hypomineralisation (MIH).
C_LIO_LITo explore for the presence of residual confounding using negative control analyses.
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MethodsThis study used data from Avon Longitudinal Study of Parents and Children (ALSPAC), a UK prospective birth cohort. We defined offspring MIH using prospectively collected questionnaire data. We used logistic regression to explore confounder adjusted associations between maternal pre-pregnancy BMI and smoking and alcohol consumption during pregnancy with MIH. We included negative control exposure (paternal BMI, smoking and alcohol around the time of pregnancy) and outcome (offspring dental trauma) analyses to explore for the presence of residual confounding.

Results5536 mother/offspring pairs were included (297 MIH cases [5.4%]). We found a positive association between maternal mean BMI and offspring MIH (OR per 1-kg/m2 difference in maternal BMI: 1.04, 95% CI: 1.00, 1.08). In subsequent analyses, we found evidence that this effect was non-linear and being driven by women in the highest BMI quintiles. In negative control analyses, we found no evidence of association between paternal BMI and offspring MIH (OR: 0.94, 95% CI: 0.89, 1.00) and maternal BMI and offspring dental trauma (OR: 0.99, 95% CI: 0.96, 1.02). There was no clear evidence of an association for maternal pregnancy smoking (OR: 0.76, 95%CI: 0.46, 1.22) and alcohol consumption (OR: 0.79, 95% CI: 0.56, 1.21) with offspring MIH with results imprecisely estimated.

ConclusionIn summary we found evidence of a possible intrauterine effect of high maternal pre-pregnancy BMI on offspring MIH. We did not find robust evidence for an intrauterine effect of maternal pregnancy smoking or alcohol consumption on offspring MIH. Our findings provide further support for women of reproductive age to maintain a healthy weight. Future studies are warranted to explore possible mechanisms on how the pregnancy environment may relate to offspring MIH. Signals of significantly increased vaccine breakthrough, decreased hospitalization rates, and less severe disease in patients with COVID-19 caused by the Omicron variant of SARS-CoV-2 in Houston, Texas Genetic variants of SARS-CoV-2 continue to dramatically alter the landscape of the COVID-19 pandemic. The recently described variant of concern designated Omicron (B.1.1.529) has rapidly spread worldwide and is now responsible for the majority of COVID-19 cases in many countries. Because Omicron was recognized very recently, many knowledge gaps exist about its epidemiology and clinical severity and disease course. A comprehensive genome sequencing study of SARS-CoV-2 in the Houston Methodist healthcare system identified 862 symptomatic patients with infections caused by Omicron from late November 2021 through December 18, 2021. Omicron very rapidly increased in only three weeks to cause 90% of all new COVID-19 cases. Compared to patients infected with either Alpha or Delta variants in our healthcare system, Omicron patients were significantly younger, had significantly increased vaccine breakthrough rates, and were significantly less likely to be hospitalized. Omicron patients required less intense respiratory support and had a shorter length of hospital stay, consistent with decreased disease severity. Although the number of Omicron patients we studied is relatively small, in the aggregate the data document the unusually rapid spread and increased occurrence of COVID-19 caused by the Omicron variant in metropolitan Houston, and provide information about disease character. S-ketamine in patient-controlled analgesia reduces opioid consumption in a dose-dependent manner after major lumbar fusion surgery: a randomized, double-blind, placebo-controlled clinical trial BACKGROUNDSpinal fusion surgery causes severe pain. Strong opioids, commonly used as postoperative analgesics, may have unwanted side effects. S-ketamine may be an effective analgesic adjuvant in opioid patient-controlled analgesia (PCA). However, the optimal adjunct S-ketamine dose to reduce postoperative opioid consumption is still unknown.

METHODSWe randomized 107 patients at two tertiary hospitals in a double-blinded, placebo-controlled clinical trial of adults undergoing major lumbar spinal fusion surgery. Patients were randomly allocated to four groups in order to compare the effects of three different doses of adjunct S-ketamine (0.25, 0.5, and 0.75 mg ml-1) or placebo on postoperative analgesia in oxycodone PCA. Study drugs were administered for 24 hours postoperative after which oxycodone-PCA was continued for further 48 hours. Our primary outcome was cumulative oxycodone consumption at 24 hours after surgery.

RESULTSOf the 100 patients analyzed, patients receiving 0.75 mg ml-1 S-ketamine in oxycodone PCA needed 25% less oxycodone at 24 h postoperatively (61.2 mg) compared with patients receiving 0.5 mg ml-1 (74.7 mg) or 0.25 mg ml-1 (74.1 mg) S-ketamine in oxycodone or oxycodone alone (81.9 mg) (mean difference: -20.6 mg; 95% confidence interval [CI]: -41 to -0.20; P = 0.048). A beneficial effect in mean change of pain intensity at rest was seen in the group receiving 0.75 mg ml-1 S-ketamine in oxycodone PCA compared with patients receiving lower ketamine doses or oxycodone alone (standardized effect size: 0.17, 95% CI: 0.013-0.32, P = 0.033). The occurrence of adverse events was similar among the groups.

CONCLUSIONSOxycodone PCA containing S-ketamine as an adjunct at a ratio of 1: 0.75 decreased cumulative oxycodone consumption at 24 h after major lumbar spinal fusion surgery without additional adverse effects. I wanna live and not think about the future; What place for advance care planning for people living with severe multiple sclerosis and their families? A qualitative study BackgroundLittle is known about how people with multiple sclerosis and their families comprehend advance care planning (ACP) and its relevance in their lives.

AimTo explore under what situations, with whom, how, and why do people with MS and their families engage in ACP

MethodsWe conducted a qualitative study comprising interviews with people living with MS and their families followed by an ethical discussion group with five health professionals representing specialties working with people affected by MS and their families. Twenty-seven people with MS and 17 family members were interviewed between June 2019 and March 2020. Interviews and the ethical discussion group were audio-recorded and transcribed verbatim. Data were analysed using the framework approach.

ResultsParticipants narratives focused on three major themes :(i) planning for an uncertain future; (ii) perceived obstacles to engaging in ACP that included uncertainty concerning MS disease progression, negative previous experiences of ACP discussions and prioritising symptom management over future planning; (iii) Preferences for engagement in ACP included a trusting relationship with a health professional and that information then be shared across services. Health professionals accounts from the ethical discussion group departed from viewing ACP as a formal document to that of an ongoing process of seeking preferences and values. They voiced similar concerns to people with MS about uncertainty and when to initiate discussions. Some shared concerns of a lack of confidence when having ACP discussions.

ConclusionThese findings support the need for a whole systems strategic approach where information about the potential benefits of ACP in all its forms can be shared with people with MS. Moreover, they highlight the need for health professionals to be skilled and trained in engaging in ACP discussions and where information is contemporaneously and seamlessly shared across services. The effect of stimulus duration on preferences for gain adjustments when listening to speech ObjectivesIn the personalisation of hearing aid fittings, gain is often adjusted to suit patient preferences using live speech. When using brief sentences as stimuli, the minimum gain adjustments necessary to elicit consistent preferences ( preference thresholds) were previously found to be much greater than typical adjustments in current practice. The current study examined the role of duration on preference thresholds.

DesignParticipants heard 2, 4 and 6-s segments of a continuous monologue presented successively in pairs. The first segment of each pair was presented at each individuals real-ear or prescribed gain. The second segment was presented with a {+/-}0-12 dB gain adjustment in one of three frequency bands. Participants judged whether the second was "better", "worse" or "no different" from the first.

Study SampleTwenty-nine adults, all with hearing-aid experience.

ResultsThe minimum gain adjustments needed to elicit "better" or "worse" judgments decreased with increasing duration for most adjustments. Inter-participant agreement and intra-participant reliability increased with increasing duration up to 4 s, then remained stable.

ConclusionsProviding longer stimuli improves the likelihood of patients providing reliable judgments of hearing-aid gain adjustments, but the effect is limited, and alternative fitting methods may be more viable for effective hearing-aid personalisation. DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine Recent global developments underscore the prominent role big data have in modern medical science. Privacy issues are a prevalent problem for collecting and sharing data between researchers. Synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue.

In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs.

In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by addressing the relevant privacy issues in medical datasets. A general computational framework for COVID-19 modelling with applications to testing varied interventions in education environments We construct a spatially-compartmental individual-based model of the spread of Covid-19 in indoor spaces. The model can be used to predict the infection rates in a variety of locations when various non-pharmaceutical interventions (NPIs) are introduced. Tasked by the Welsh Government, we apply the model to secondary schools and Further and Higher Education environments. Specifically, we consider student populations mixing in a classroom and in halls of residence. We focus on assessing the potential efficacy of Lateral Flow Devices (LFDs) when used in broad-based screens for asymptomatic infection or in  test-to-release scenarios in which individuals who have been exposed to infection are released from isolation given a negative result. LFDs are also compared to other NPIs; we find that, although LFD testing can be used to mitigate the spread of Covid-19, it is more effective to invest in personal protective equipment, e.g. masks, and in increasing ventilation quality. In addition, we provide an open-access and user-friendly online applet that simulates the individual-based model, complete with user tutorials to encourage the use of the model to aid educational policy decisions as input infection data evolves (https://bit.ly/CV19_INTER_IBM). Factors affecting first return to work following arthroscopic rotator cuff repair: A retrospective observational study in an occupational medicine clinic and single-surgeon practice BackgroundReturn to work (RTW) following arthroscopic rotator cuff repair (aRCR) within New South Wales (NSW), Australia, under compensable schemes has not been reported.

AimsDetermine factors affecting RTW status and time in patients treated with aRCR under state-based compensation schemes, compared to those outside such schemes.

MethodsPatients undergoing aRCR by one surgeon with minimum 1-year follow-up were grouped into those under (CP) or outside (non-CP) workers or vehicle accident compensation schemes, matched by age and gender. RTW status and time were assessed using chi-square analysis and multivariable linear regression.

ResultsOf 1054 available patients, 90 CP patients were identified with 29 consented and matched to non-CP (N=29). A higher proportion of CP patients (17.2 vs 0%, P<0.001) never returned to work and a lower proportion resumed pre-injury duties at first RTW (3 vs 52%, P<0.01). Median time to first RTW did not differ between CP and non-CP groups (5.1 vs 4.4, P=0.86). Smoking (P=0.007) and post-injury activity level (P=0.004) were significantly associated with longer time to first RTW, whereas compensation status was not.

ConclusionsCP patients undergoing aRCR in NSW are at risk of not returning to work. For those that return, there is no significant difference compared to non-CP in time to first RTW. Particularly, patient and management factors associated with extended time to first RTW have been identified. Interventions aimed at modifiable factors such as smoking cessation and increasing preoperative activity may improve future outcomes.

What is already known about this subjectO_LIWork-related injury and disease present a significant financial burden to the Australian economy ($AUD61.8billion per annum), with shoulder conditions due to body stressing accounting for 7% of this total cost (>$AUD4.2billion)
C_LIO_LIPatients receiving compensation appear to have poorer outcomes and take longer to return to work than their non-compensated counterparts, but the degree to which this occurs is variably reported in the literature
C_LIO_LIThe effect of modifiable factors on return to work status and time remain unclear, and have not previously been reported for the Australian population under the New South Wales workers compensation scheme
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What this study addsO_LIPatients receiving arthroscopic rotator cuff repair under a compensable scheme in New South Wales are at increased risk of not returning to work compared to patients presenting outside of a compensable scheme and patients in equivalent schemes internationally
C_LIO_LIPatients reporting a longer duration of symptoms and smokers are at increased risk of extended time to first return to work
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What impact this may have on practice or policyO_LIThe study has identified smoking and pre-surgery activity levels as key targets for intervention in patients receiving arthroscopic rotator cuff repair under a workers compensation scheme in New South Wales
C_LIO_LIBy further investigating whether interventions designed to modify these factors are able to favourably affect patient outcomes, there is potential to return patients to their preoperative level of work sooner
C_LI Telecommuting frequency and preference among Japanese workers according to regional cumulative COVID-19 incidence: a cross-sectional study This study aimed to examine the relationship between telecommuting and the regional cumulative COVID-19 incidence. This was a cross-sectional study analyzing 13,468 office workers. The participant groups, according to the level of cumulative COVID-19 incidence by prefecture, were used as the predictor variable, and telecommuting frequency and preference were used as outcomes. We employed an ordinal logistic regression analysis. In regions with a high cumulative COVID-19 incidence, the proportion of participants who telecommuted more than two days per week was 34.7%, which was approximately 20% higher than in other regions. Telecommuting preference was stronger in areas with higher COVID-19 influence. However, in other regions, the proportion of participants who did not want to telecommute was higher than that of those who wanted to telecommute. We found that telecommuting frequency and preference were higher in regions with high cumulative COVID-19 incidence. "What do you mean I can't have a doctor? This is Canada!" - A Qualitative Study of the Myriad Consequences for Unattached Patients Awaiting Primary Care Attachment BackgroundPatient access to primary healthcare (PHC) is the foundation of a strong healthcare system and healthy populations. Attachment to a regular PHC provider, a key to healthcare access, has seen a decline in some jurisdictions. This study explored the consequences of unattachment from a patient perspective, an under-studied phenomenon to date.

MethodsA realist-informed qualitative study was conducted with unattached patients in Nova Scotia, Canada. Semi-structured interviews with nine participants were conducted and transcribed for analysis. The framework method was used to carry out analysis, which was guided by Donabedians model of assessing healthcare access and quality.

ResultsFive key findings were noted in this study: 1) Participants experienced a range of consequences from not having a regular PHC provider. Participants used creative strategies to 2) attempt to gain attachment to a regular PHC provider, and, to 3) address their health needs in the absence of a regular PHC provider. 4) Participants experienced negative feelings about themselves and the healthcare system, and 5) stress related to the consequences and added work of being unattached and lost care.

ConclusionsUnattached patients experienced a burden of care related to lost care and managing their own health and related information, due to the download of medical record management and system navigation to them. These findings may underestimate the consequences for further at-risk populations who would not have been included in our recruitment. This may result in poorer health outcomes, which could be mitigated by interventions at the structural level, such as enhanced centralized waitlists to promote attachment. Such waitlists may benefit from a triage approach to appropriately attach patients based on need. Substance Use Onset in High-Risk 9-13 Year-Olds in the ABCD Study AimA key aim of the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study is to document substance use onset, patterns, and sequelae across adolescent development. However, substance use misreporting obscures accurate drug use characterization. Hair toxicology tests provide objective historical substance use data, but are rarely investigated in youth. Here, we compare objective hair toxicology results with self-reported substance use in youth.

MethodsA literature-based substance use risk algorithm identified 696 ABCD Study(R) participants for hair sample collections between baseline and 2-year follow-up (spanning ages 9-13) for laboratory analysis. Chi-square and t-tests assessed differences between participants demographics, positive and negative hair tests, risk algorithm scores, and self-reported substance use.

ResultsHair testing confirmed that 17% of at-risk 9-13 year-olds had evidence of past 3-month use of one (n=99), two (n=17), three (n=3), or four (n=2) drug classes. After considering prescribed medication use, 11% had a positive test incongruent with self- or parent-report. No participant with a positive result self-reported substance use consistent with their toxicology results. Participants with positive tests under-reported use (p<.001), reported less sipping of alcohol (p<.001), and scored higher on the risk algorithm (p<.001) than those with negative hair toxicology.

ConclusionsAn alarming 11% of tested samples in at-risk 9-13 year-olds were positive for at least one unreported substance, suggesting underreporting in this population when participating in a national healthy development study. The degree of underreporting cannot yet be calculated, as at-risk samples were prioritized for assays. Expanded toxicology testing is key to characterize substance use in youth. Prospectively validated disease-agnostic predictive medicine with augmented intelligence Despite numerous superhuman achievements in complex challenges1,2, standalone AI does not free life science from the long-term bottleneck of linearly extracting new knowledge from exponentially growing new data3, severely limiting the success rate of drug discovery4. Inspired by the state-of-the-art AI training methods, we trained a human-centric hybrid augmented intelligence5 (HAI) to learn a foundation model6 that extracts all-encompassing knowledge of human physiology and diseases. To evaluate the quality of HAIs extracted knowledge, we designed the public, prospective prediction of pivotal ongoing clinical trial outcomes at large scale (PROTOCOLS) challenge to benchmark HAIs real-world performance of predicting drug clinical efficacy without access to human data. HAI achieved a 10.5-fold improvement from the baseline with 99% confidence7 in the PROTOCOLS validation, readily increasing the average clinical success rate of investigational new drugs from 7.9%8 to 90% for almost any human diseases9,10. The validated HAI confirms that exponentially extracted knowledge alone is sufficient for accurately predicting drug clinical efficacy, effecting a total reversal of Erooms law4. HAI is also the worlds first clinically validated model of human aging that could substantially speed up the discovery of preventive medicine for all age-related diseases11. Our results demonstrate that disruptive breakthroughs necessitate the smallest team size to attain the largest HAI for optimal knowledge extraction from high-dimensional low-quality data space, thus establishing the first prospective proof of the previous discovery that small teams disrupt12. The global adoption of training HAI provides a well-beaten economic path to mass-produce scientific and technological breakthroughs via exponential knowledge extraction and better designs of data labels for training better-performing AI13.