Open science and conflict of interest policies of medical and health sciences journals before and during the COVID-19 pandemic: A repeat cross-sectional study: Open science policies of medical journals.

Objectives: To audit the transparent and open science standards of health and medical sciences journal policies and explore the impact of the COVID-19 pandemic. Design: Repeat cross-sectional study. Setting: 19 journals listed in Google Scholar's Top Publications for health and medical sciences. Participants: Blood, Cell, Circulation, European Heart Journal, Gastroenterology, Journal of Clinical Oncology, Journal of the American College of Cardiology, Nature Genetics, Nature Medicine, Nature Neuroscience, Neuron, PLoS ONE, Proceedings of the National Academy of Sciences, Science Translational Medicine, The British Medical Journal, The Journal of the American Medical Association, The Lancet, The Lancet Oncology, and The New England Journal of Medicine. Main outcome measures: We used the Transparency and Openness Promotion (TOP) guideline and the International Committee of Medical Journal Editors (ICMJE) requirements for disclosing conflicts of interest (COIs) to evaluate journals standards. Results: TOP scores slightly improved during the COVID-19 pandemic, from a median of 5 (IQR: 2-12.5) out of a possible 24 points in February 2020 to 7 (IQR: 4-12) in May 2021, but overall, scores were very low at both time points. Journal policies scored highest for their adherence to data transparency and scored lowest for preregistration of study protocols and analysis plans and the submission of replication studies. Most journals fulfilled all ICMJE provisions for reporting COIs before (84%; n = 16) and during (95%; n = 18) the COVID-19 pandemic. Conclusions: The COVID-19 pandemic has highlighted the importance of practising open science. However, requirements for open science practices in audited policies were overall low, which may impede progress in health and medical research. As key stakeholders in disseminating research, journals should promote a research culture of greater transparency and more robust open science practices.

High quality (certainty) evidence changes less often than low-quality evidence, but the magnitude of effect size does not systematically differ between studies with low versus high-quality evidence.

RATIONALE, AIMS, AND OBJECTIVES: It is generally believed that evidence from low quality of evidence generate inaccurate estimates about treatment effects more often than evidence from high (certainty) quality evidence (CoE). As a result, we would expect that (a) estimates of effects of health interventions initially based on high CoE change less frequently than the effects estimated by lower CoE (b) the estimates of magnitude of effect size differ between high and low CoE. Empirical assessment of these foundational principles of evidence-based medicine has been lacking. METHODS: We reviewed the Cochrane Database of Systematic Reviews from January 2016 through May 2021 for pairs of original and updated reviews for change in CoE assessments based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method. We assessed the difference in effect sizes between the original versus updated reviews as a function of change in CoE, which we report as a ratio of odds ratio (ROR). We compared ROR generated in the studies in which CoE changed from very low/low (VL/L) to moderate/high (M/H) versus M/H to VL/L. Heterogeneity and inconsistency were assessed using the tau and I2 statistic. We also assessed the change in precision of effect estimates (by calculating the ratio of standard errors) (seR), and the absolute deviation in estimates of treatment effects (aROR). RESULTS: Four hundred and nineteen pairs of reviews were included of which 414 (207 × 2) informed the CoE appraisal and 384 (192 × 2) the assessment of effect size. We found that CoE originally appraised as VL/L had 2.1 [95% confidence interval (CI): 1.19-4.12; p = 0.0091] times higher odds to be changed in the future studies than M/H CoE. However, the effect size was not different (p = 1) when CoE changed from VL/L → M/H [ROR = 1.02 (95% CI: 0.74-1.39)] compared with M/H → VL/L (ROR = 1.02 [95% CI: 0.44-2.37]). Similar overlap in aROR between the VL/L → M/H versus M/H → VL/L subgroups was observed [median (IQR): 1.12 (1.07-1.57) vs. 1.21 (1.12-2.43)]. We observed large inconsistency across ROR estimates (I2 = 99%). There was larger imprecision in treatment effects when CoE changed from VL/L → M/H (seR = 1.46) than when it changed from M/H → VL/L (seR = 0.72). CONCLUSIONS: We found that low-quality evidence changes more often than high CoE. However, the effect size did not systematically differ between the studies with low versus high CoE. The finding that the effect size did not differ between low and high CoE indicate urgent need to refine current EBM critical appraisal methods.

Much ado about something: a response to "COVID-19: underpowered randomised trials, or no randomised trials?"

Non-pharmaceutical interventions (NPI) for infectious diseases such as COVID-19 are particularly challenging given the complexities of what is both practical and ethical to randomize. We are often faced with the difficult decision between having weak trials or not having a trial at all. In a recent article, Dr. Atle Fretheim argues that statistically underpowered studies are still valuable, particularly in conjunction with other similar studies in meta-analysis in the context of the DANMASK-19 trial, asking "Surely, some trial evidence must be better than no trial evidence?" However, informative trials are not always feasible, and feasible trials are not always informative. In some cases, even a well-conducted but weakly designed and/or underpowered trial such as DANMASK-19 may be uninformative or worse, both individually and in a body of literature. Meta-analysis, for example, can only resolve issues of statistical power if there is a reasonable expectation of compatible well-designed trials. Uninformative designs may also invite misinformation. Here, we make the case that-when considering informativeness, ethics, and opportunity costs in addition to statistical power-"nothing" is often the better choice.

What items should be included in an early warning score for remote assessment of suspected COVID-19? qualitative and Delphi study.

BACKGROUND: To develop items for an early warning score (RECAP: REmote COVID-19 Assessment in Primary Care) for patients with suspected COVID-19 who need escalation to next level of care. METHODS: The study was based in UK primary healthcare. The mixed-methods design included rapid review, Delphi panel, interviews, focus groups and software development. Participants were 112 primary care clinicians and 50 patients recovered from COVID-19, recruited through social media, patient groups and snowballing. Using rapid literature review, we identified signs and symptoms which are commoner in severe COVID-19. Building a preliminary set of items from these, we ran four rounds of an online Delphi panel with 72 clinicians, the last incorporating fictional vignettes, collating data on R software. We refined the items iteratively in response to quantitative and qualitative feedback. Items in the penultimate round were checked against narrative interviews with 50 COVID-19 patients. We required, for each item, at least 80% clinician agreement on relevance, wording and cut-off values, and that the item addressed issues and concerns raised by patients. In focus groups, 40 clinicians suggested further refinements and discussed workability of the instrument in relation to local resources and care pathways. This informed design of an electronic template for primary care systems. RESULTS: The prevalidation RECAP-V0 comprises a red flag alert box and 10 assessment items: pulse, shortness of breath or respiratory rate, trajectory of breathlessness, pulse oximeter reading (with brief exercise test if appropriate) or symptoms suggestive of hypoxia, temperature or fever symptoms, duration of symptoms, muscle aches, new confusion, shielded list and known risk factors for poor outcome. It is not yet known how sensitive or specific it is. CONCLUSIONS: Items on RECAP-V0 align strongly with published evidence, clinical judgement and patient experience. The validation phase of this study is ongoing. TRIAL REGISTRATION NUMBER: NCT04435041.

Diabetes and COVID-19: Risks, Management, and Learnings From Other National Disasters.

Evidence relating to the impact of COVID-19 in people with diabetes (PWD) is limited but continuing to emerge. PWD appear to be at increased risk of more severe COVID-19 infection, though evidence quantifying the risk is highly uncertain. The extent to which clinical and demographic factors moderate this relationship is unclear, though signals are emerging that link higher BMI and higher HbA1c to worse outcomes in PWD with COVID-19. As well as posing direct immediate risks to PWD, COVID-19 also risks contributing to worse diabetes outcomes due to disruptions caused by the pandemic, including stress and changes to routine care, diet, and physical activity. Countries have used various strategies to support PWD during this pandemic. There is a high potential for COVID-19 to exacerbate existing health disparities, and research and practice guidelines need to take this into account. Evidence on the management of long-term conditions during national emergencies suggests various ways to mitigate the risks presented by these events.