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Background The relationship between the presence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the risk of subsequent reinfection remains unclear. Methods We investigated the incidence of SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) in seropositive and seronegative health care workers attending testing of asymptomatic and symptomatic staff at Oxford University Hospitals in the United Kingdom. Baseline antibody status was determined by anti-spike (primary analysis) and anti-nucleocapsid IgG assays, and staff members were followed for up to 31 weeks. We estimated the relative incidence of PCR-positive test results and new symptomatic infection according to antibody status, adjusting for age, participant-reported gender, and changes in incidence over time. Results A total of 12,541 health care workers participated and had anti-spike IgG measured; 11,364 were followed up after negative antibody results and 1265 after positive results, including 88 in whom seroconversion occurred during follow-up. A total of 223 anti-spike–seronegative health care workers had a positive PCR test (1.09 per 10,000 days at risk), 100 during screening while they were asymptomatic and 123 while symptomatic, whereas 2 anti-spike–seropositive health care workers had a positive PCR test (0.13 per 10,000 days at risk), and both workers were asymptomatic when tested (adjusted incidence rate ratio, 0.11; 95% confidence interval, 0.03 to 0.44; P=0.002). There were no symptomatic infections in workers with anti-spike antibodies. Rate ratios were similar when the anti-nucleocapsid IgG assay was used alone or in combination with the anti-spike IgG assay to determine baseline status. Conclusions The presence of anti-spike or anti-nucleocapsid IgG antibodies was associated with a substantially reduced risk of SARS-CoV-2 reinfection in the ensuing 6 months. (Funded by the U.K. Government Department of Health and Social Care and others.)

Distance-learning increasingly forms part of many academic and professional courses. OxPal, a collaboration between University of Oxford medical students and doctors and their counterparts in the occupied Palestinian territory (oPt), sought to evaluate the feasibility and efficacy of interactive online distance-learning through a pre-established international partnership as a method for rapid knowledge sharing during the novel coronavirus disease 2019 (COVID-19) response. Two interactive online lectures for medical students and clinicians in the oPt on the COVID-19 pandemic were conducted. The first lecture was an “Introduction to COVID-19” and the second focused on “Data-sharing during the pandemic”. 212 and 174 attended each lecture, respectively. Feedback was via an online questionnaire. >95% of respondents indicated the lectures covered a gap in their medical education. 87% and 77% of respondents rated lecture quality “Good” or “Excellent” for the two lectures respectively. Qualitative feedback elicited requests for more clinically focused lectures, which have since been provided. Online lectures are feasible and effective for rapid education of medical students and clinicians in the oPt in a public health emergency. We hope this encourages other institutions to provide similar support in the oPt and other ‘tutor-deplete’ regions facing specific geopolitical challenges to local medical education.

Background Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12–24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department. Methods We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC). Findings 72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858–0·881, 95% CI 0·838–0·912, for CURIAL-Lab and 0·836–0·854, 0·814–0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5–85·7, for CURIAL-Lab and 83·5%, 81·8–85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9–71·8) for CURIAL-Lab and 63·6% (63·1–64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7–62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6–88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4–91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32–64), 16 min (26·3%) sooner than with LFDs (61 min, 37–99; log-rank p Interpretation Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas.

Background: Shift work is essential in society but can be detrimental to health and quality of life and is associated with decreased productivity and increased risk of accidents. Interventions to reduce these consequences are needed, but the extent and range of trial evidence for interventions for those most affected by their shift-work schedules is unclear. We therefore carried out a scoping review to assess the availability of evidence to inform the development and evaluation of future interventions. Methods: We aimed to identify clinical trials of any intervention for shift work-related sleep disturbance that included a comparator group, where the intervention was delivered in an occupational setting. We searched Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, CINAHL, EMBASE, Medline and Science Citation Index from inception to 30th March 2020 for relevant citations. Citations were screened by two independent reviewers, a third reviewer resolved disagreements. Data were extracted by two independent reviewers. Results: From 1250 unique citations, 14 studies met inclusion criteria for comparative trials of treatment in an occupational setting. There were five trials of hypnotics, five trials of stimulants, and four trials of non-pharmacological therapies (cognitive behavioural therapy, light therapy, aromatherapy and herbal medicine). Outcomes included sleep parameters, day-time sleepiness, and quality of life. There were no consistently reported outcomes across trials. Conclusions: Interventions fell into three distinct groups investigated in distinct time periods without progression from efficacy trials to wider-scale interventions. The lack of consistent patient-reported outcome measures limits synthesising findings. Some trials focussed on optimising sleep, others on reducing wake-time sleepiness. Adequately powered trials of existing interventions are needed, with the development and testing of novel combination treatments in patients with well-defined shift work sleep disorder. A core set of clinically relevant outcomes will develop and standardise the evidence-base for shift work sleep disorder.