Other Respiratory Viruses Are Important Contributors to Adult Respiratory Hospitalizations and Mortality Even During Peak Weeks of the Influenza Season
Bibliographic record
Abstract
BACKGROUND: During peak weeks of seasonal influenza epidemics, severe respiratory infections without laboratory confirmation are typically attributed to influenza. METHODS: In this prospective study, specimens and demographic and clinical data were collected from adults admitted with respiratory symptoms to 4 hospitals during the 8-10 peak weeks of 2 influenza seasons. Specimens were systematically tested for influenza and 13 other respiratory viruses (ORVs) by using the Luminex RVP FAST assay. RESULTS: At least 1 respiratory virus was identified in 46% (21% influenza, 25% noninfluenza; 2% coinfection) of the 286 enrolled patients in 2011-2012 and in 62% (46% influenza, 16% noninfluenza; 3% coinfection) of the 396 enrolled patients in 2012-2013. Among patients aged ≥75 years, twice as many ORVs (32%) as influenza viruses (14%) were detected in 2011-2012. During both seasons, the most frequently detected ORVs were enteroviruses/rhinoviruses (7%), respiratory syncytial virus (6%), human metapneumovirus (5%), coronaviruses (4%), and parainfluenza viruses (2%). Disease severity was similar for influenza and ORVs during both seasons. CONCLUSIONS: Although ORV contribution relative to influenza varies by age and season, during the peak weeks of certain influenza seasons, ORVs may be a more frequent cause of elderly hospitalization than influenza.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".