Detection of RSV using nasopharyngeal swabs alone underestimates RSV-related hospitalization incidence in adults: the Multispecimen study’s Final Analysis
Bibliographic record
Abstract
ABSTRACT Background RSV detection improves if an additional specimen is collected, but the impact of testing saliva and multiple specimen types has not been assessed. We quantified RSV detection increase with multiple specimen collection over nasopharyngeal swab (NPS) alone. Methods Prospectively enrolled hospitalized adults aged ≥40 years with acute respiratory illness in seven hospitals in US and Canada had NPS, saliva, sputa, and acute/convalescent sera collected and tested. Results Among 3,669 enrolled participants, 100% had NPS, 97.7% saliva, 33.0% sputum, and 33.4% paired serology. RSV detection was 112% higher (95% CI86%−141%) using all specimen types compared to NPS alone. Saliva had higher sensitivity than NPS (61.4% versus 47.2%). Among those with congestive heart failure exacerbations, additional specimens increased RSV detection by 267% (95% CI85%−625%) and saliva detected more infections than NPS. Among 1013 subjects with paired NPS from different timepoints tested on the same platform, specimens collected on average 1 day later detected 30% less RSV infections. Conclusions RSV detection increased over 100% using four specimen types versus NPS alone, suggesting a 2-fold correction factor is appropriate for incidence/prevalence studies relying on NPS alone. Saliva is more sensitive than NPS, warranting further study particularly in cardiac patients.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".