Detection by Nasopharyngeal Swabs Alone Underestimates Respiratory Syncytial Virus–Related Hospitalization Incidence in Adults: The Multispecimen Study's Final Analysis
Bibliographic record
Abstract
BACKGROUND: Most epidemiologic studies and clinical testing use single nasal/nasopharyngeal swab (NPS) for respiratory syncytial virus (RSV) detection. Studies document that RSV detection improves if another specimen is added to NPS, but the impact of using multiple specimen types has not been assessed. We quantified RSV detection increase using multiple-specimen testing versus NPS alone. METHODS: Hospitalized adults aged ≥40 years with acute respiratory illness were prospectively enrolled in 7 US/Canadian hospitals. NPS, saliva, sputa, and acute/convalescent sera were collected and tested. RESULTS: Among 3669 participants, 100% had NPS, 97.7% saliva, 33.0% sputum, and 33.4% paired serology. RSV detection was 112% higher (95% CI, 86%-141%) using all specimen types as compared with NPS alone. Serology test sensitivity was the highest (73.0%; 95% CI, 65.1%-80.8%), followed by sputum (70.1%; 95% CI, 62.1%-78.0%), saliva (61.4%; 95% CI, 55.4%-67.5%), and NPS (47.2%; 95% CI, 41.1%-53.4%). Among those with congestive heart failure exacerbations, additional specimens increased detection by 267% (95% CI, 85%-625%), and saliva detected more infections than NPS. Among 1013 participants with paired NPSs from different time points, specimens collected on average 1 day later detected 30% fewer RSV infections. CONCLUSIONS: RSV detection increased >100% using 4 specimen types, suggesting a 2-fold correction factor is appropriate for incidence and prevalence studies relying on NPS alone. Saliva was more sensitive than NPS, warranting further study, particularly in cardiac cases.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".