Diagnostic Accuracy of Rapid Antigen Detection Tests for Respiratory Syncytial Virus Infection: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) rapid antigen detection tests (RADT) are extensively used in clinical laboratories. We performed a systematic review and meta-analysis to evaluate the accuracy of RADTs for diagnosis of RSV infection and to determine factors associated with accuracy estimates. We searched EMBASE and PubMed for diagnostic-accuracy studies of commercialized RSV RADTs. Studies reporting sensitivity and specificity data compared to a reference standard (reverse transcriptase PCR [RT-PCR], immunofluorescence, or viral culture) were considered. Two reviewers independently extracted data on study characteristics, diagnostic-accuracy estimates, and study quality. Accuracy estimates were pooled using bivariate random-effects regression models. Heterogeneity was investigated with prespecified subgroup analyses. Seventy-one articles met inclusion criteria. Overall, RSV RADT pooled sensitivity and specificity were 80% (95% confidence interval [CI], 76% to 83%) and 97% (95% CI, 96% to 98%), respectively. Positive- and negative-likelihood ratios were 25.5 (95% CI, 18.3 to 35.5) and 0.21 (95% CI, 0.18 to 0.24), respectively. Sensitivity was higher in children (81% [95% CI, 78%, 84%]) than in adults (29% [95% CI, 11% to 48%]). Because of this disparity, further subgroup analyses were restricted to pediatric data (63 studies). Test sensitivity was poorest using RT-PCR as a reference standard and highest using immunofluorescence (74% versus 88%; P < 0.001). Industry-sponsored studies reported significantly higher sensitivity (87% versus 78%; P = 0.01). Our results suggest that the poor sensitivity of RSV RADTs in adults may preclude their use in this population. Furthermore, industry-sponsored studies and those that did not use RT-PCR as a reference standard likely overestimated test sensitivity.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".