Evaluation of the QuickLab RSV Test, a New Rapid Lateral-Flow Immunoassay for Detection of Respiratory Syncytial Virus Antigen
Bibliographic record
Abstract
Rapid respiratory syncytial virus (RSV) diagnosis is vital to the prevention of nosocomial RSV infections. We evaluated a new rapid lateral-flow RSV immunoassay, the QuickLab RSV test, that requires use of only one reagent. We compared QuickLab to the Directigen RSV (DIR) assay, which requires six reagents, and direct fluorescent antibody (DFA) testing. DFA results were considered the "gold standard." For 133 nasopharyngeal aspirates tested, DFA results were 77 (57.8%) positive, 47 (35.3%) negative, and 9 (6.8%) indeterminate. The sensitivities, specificities, positive predictive values, and negative predictive values of QuickLab and DIR tests were 93.3% (70 of 75) and 80.8% (59 of 73), 95.6% (43 of 45) and 100.0% (46 of 46), 97.2% (70 of 72) and 100.0% (59 of 59), and 89.6% (43 of 48) and 76.7% (46 of 60), respectively. QuickLab was significantly (P = 0.02) more sensitive than DIR; the difference in specificities was not significant. DFA was more sensitive than DIR (P < 0.001) but not more sensitive than QuickLab (P = 0.45). The results of DIR testing were initially uninterpretable and required retesting with 15% of the specimens compared to 3% of QL results (P < 0.001). We conclude that the QuickLab RSV test has sensitivity similar to that of the DFA assay and better than that of the DIR assay. QuickLab testing is also simpler to perform and interpret than both DFA and DIR testing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".