Cognitive Bias and the Creation and Translation of Evidence Into Clinical Practice

Preprints in times of COVID19: the time is ripe for agreeing on terminology and good practices

Over recent years, the research community has been increasingly using preprint servers to share manuscripts that are not yet peer-reviewed. Even if it enables quick dissemination of research findings, this practice raises several challenges in publication ethics and integrity. In particular, preprints have become an important source of information for stakeholders interested in COVID19 research developments, including traditional media, social media, and policy makers. Despite caveats about their nature, many users can still confuse pre-prints with peer-reviewed manuscripts. If unconfirmed but already widely shared first-draft results later prove wrong or misinterpreted, it can be very difficult to "unlearn" what we thought was true. Complexity further increases if unconfirmed findings have been used to inform guidelines. To help achieve a balance between early access to research findings and its negative consequences, we formulated five recommendations: (a) consensus should be sought on a term clearer than 'pre-print', such as 'Unrefereed manuscript', "Manuscript awaiting peer review" or ''Non-reviewed manuscript"; (b) Caveats about unrefereed manuscripts should be prominent on their first page, and each page should include a red watermark stating 'Caution-Not Peer Reviewed'; (c) pre-print authors should certify that their manuscript will be submitted to a peer-review journal, and should regularly update the manuscript status; (d) high level consultations should be convened, to formulate clear principles and policies for the publication and dissemination of non-peer reviewed research results; (e) in the longer term, an international initiative to certify servers that comply with good practices could be envisaged.

Clinical Prediction of Coronavirus Disease-2019: How Accurate Can One Be?

Background Some models based on clinical information have been reported to predict which patients have Coronavirus Disease-2019 (COVID-19) pneumonia but have failed so far to yield reliable results. We aimed to determine if physicians were able to accurately predict which patients, as described in clinical vignettes, had, or did not have this infection using their clinical acumen and epidemiological data. Methods Of 1177 patients under investigation for COVID-19 admitted, we selected 20 and presented them in a vignette form. We surveyed physicians from different levels of training (<5, and five or more years after graduation from medical school) and included non-medical participants as a control group. We asked all participants to predict the result of the PCR test for COVID-19. We measured the accuracy of responses as a whole, and at three stages of the pandemic associated with a growing incidence of COVID-19 in the community. We calculated the inter-rater reliability, sensitivity, and specificity of the clinical prediction as a whole and by pandemic stage.  Results Between June 8 and August 28, 2020, 82 doctors and 20 non-medical participants completed the survey. The accuracy was 58% (59% for doctors and 52% for non-medical, p=0.002). The lowest accuracy was noted for cases in the pandemic middle stage; years of post-graduate training represented no difference. Of the 2040 total answers, 1176 were accurate and 864 inaccurate (349 false positives and 515 false negatives). Conclusion The influence of symptomatic positivity, confirmation bias, and rapid expertise acquisition on accuracy is discussed, as the disease is new, time after graduation made no difference in the response accuracy. The limited clinical diagnostic capacity emphasizes the need for a reliable diagnostic test.