Performance of different rapid antigen testing strategies for SARS‐CoV‐2: A living rapid review
Bibliographic record
Abstract
BACKGROUND: Rapid antigen detection tests (RADTs) for SARS-CoV-2 testing offer several advantages over molecular tests, but there is little evidence supporting an ideal testing algorithm. We aimed to examine the diagnostic test accuracy (DTA) and the effectiveness of different RADT SARS-CoV-2 testing strategies. METHODS: Following PRISMA DTA guidance, we carried out a living rapid review and meta-analysis. Searches were conducted in Ovid MEDLINE® ALL, Embase and Cochrane CENTRAL electronic databases until February 2022. Results were visualized using forest plots and included in random-effects univariate meta-analyses, where eligible. RESULTS: After screening 8010 records, 18 studies were included. Only one study provided data on incidence outcomes. Seventeen studies were DTA reports with direct comparisons of RADT strategies, using RT-PCR as the reference standard. Testing settings varied, corresponding to original SARS-CoV-2 or early variants. Strategies included differences in serial testing, the individual collecting swabs and swab sample locations. Overall, specificity remained high (>98%) across strategies. Although results were heterogeneous, the sensitivity for healthcare worker-collected samples was greater than for self-collected samples. Nasal samples had comparable sensitivity when compared to paired RADTs with nasopharyngeal samples, but sensitivity was much lower for saliva samples. The limited evidence for serial testing suggested higher sensitivity if RADTs were administered every 3 days compared to less frequent testing. CONCLUSIONS: Additional high-quality research is needed to confirm our findings; all studies were judged to be at risk of bias, with significant heterogeneity in sensitivity estimates. Evaluations of testing algorithms in real-world settings are recommended, especially for transmission and incidence outcomes.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".