Guidance for the design and reporting of studies evaluating the clinical performance of tests for present or past SARS-CoV-2 infection
Bibliographic record
Abstract
Testing for SARS-CoV-2 infection is key in managing the current pandemic. More than 1700 preprints and peer reviewed journal articles evaluating tests for SARS-CoV-2 infection have been published as of January 2021. However, evaluations of these studies have identified many methodological issues, leading to a high risk of bias and difficulties applying the results in practice. Better guidance is urgently needed on the conduct and interpretation of these studies. This article outlines the principles for defining the intended purpose of the test; study population selection; reference standard, test timing; and other critical considerations for the design, reporting, and interpretation of diagnostic accuracy studies. The implementation and accuracy of SARS-CoV-2 tests have major implications for individuals and communities, balancing the potential consequences of continued infection against the need for public health measures, such as the restriction of movements and social activities. Decision making in the current pandemic requires a clear understanding of the clinical performance and limitations of testing. This article provides guidance to assist researchers design robust diagnostic accuracy studies, assist publishers and peer reviewers to assess such studies, and support clinicians and policy makers in their evaluation of the evidence on SARS-CoV-2 testing for clinical and public health decisions. The guidance aims to ensure that studies evaluating the diagnostic accuracy of SARS-CoV-2 tests are conducted as rigorously as possible, in an efficient and timely way.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.465 | 0.693 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.016 | 0.007 |
| Research integrity | 0.039 | 0.024 |
| Insufficient payload (model declined to judge) | 0.020 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".