Risk of bias and limits of reporting in diagnostic accuracy studies for commercial point-of-care tests for respiratory pathogens
Bibliographic record
Abstract
Commercial point-of-care (POC) diagnostic tests for Group A Streptococcus, Streptococcus pneumoniae, and influenza virus have large potential diagnostic and financial impact. Many published reports on test performance, often funded by diagnostics companies, are prone to bias. The Standards for Reporting of Diagnostic Accuracy (STARD 2015) are a protocol to encourage accurate, transparent reporting. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool evaluates risk of bias and transportability of results. We used these tools to evaluate diagnostic test accuracy studies of POC studies for three respiratory pathogens. For the 96 studies analysed, compliance was <25% for 14/34 STARD 2015 standards, and 3/7 QUADAS-2 domains showed a high risk of bias. All reports lacked reporting of at least one criterion. These biases should be considered in the interpretation of study results.
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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 | 0.867 | 0.938 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".