Accuracy of package inserts of SARS-CoV-2 rapid antigen tests: a secondary analysis of manufacturer versus systematic review data
Bibliographic record
Abstract
BACKGROUND: Rapid antigen tests (RATs) were crucial during the COVID-19 pandemic. Information provided by the test manufacturer in product package inserts, also known as instructions for use (IFUs), is often the only data available to clinicians, public health professionals, and individuals on the diagnostic accuracy of these tests. We aimed to assess whether manufacturer IFU accuracy data aligned with evidence from independent research. METHODS: We searched company websites for package inserts for RATs that were included in the July 2022 update of the Cochrane meta-analysis of SARS-CoV-2 RATs, which served as a benchmark for research evidence. We fitted bivariate hierarchical models to obtain absolute differences in sensitivity and specificity between IFU and Cochrane Review estimates for each test, as well as overall combined differences. FINDINGS: We found 22 (100%) of 22 IFUs of the RATs included in the Cochrane Review. IFUs for 12 (55%) of 22 RATs reported statistically significantly higher sensitivity estimates than the Cochrane Review, and none reported lower estimates. The mean difference between IFU and Cochrane Review sensitivity estimates across tests was 12·0% (95% CI 7·5-16·6). IFUs in three (14%) of 22 diagnostic tests had significantly higher specificity estimates than the Cochrane Review and two (9%) of 22 had lower estimates. The mean difference between IFU and Cochrane Review specificity estimates across tests was 0·3% (95% CI 0·1-0·5). If 100 people with SARS-CoV-2 infection were tested with each of the tests in this study, on average 12 fewer people would be correctly diagnosed than is suggested by the package inserts. INTERPRETATION: Health professionals and the public should be aware that package inserts for SARS-CoV-2 RATs might provide an overly optimistic picture of the sensitivity of a test. Regulatory bodies should strengthen their requirements for the reporting of diagnostic accuracy data in package inserts and policy makers should demand independent validation data for decision making. FUNDING: None.
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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 | 0.053 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".