Comparison of Risk Factors for Human Metapneumovirus and Respiratory Syncytial Virus Disease Severity in Young Children
Bibliographic record
Abstract
BACKGROUND: Human metapneumovirus (hMPV) and respiratory syncytial virus (RSV) are leading pediatric pathogens. However, risk factors for severe hMPV disease remain unknown. We comparatively assessed environmental, host, and viral determinants for severe hMPV and RSV infections. METHODS: We studied a prospective cohort of >1000 children aged <3 years hospitalized in or presenting to a pediatric clinic for acute respiratory infection. We collected clinical data at enrollment and 1-month follow-up and tested nasopharyngeal secretions for respiratory viruses. Disease severity was defined as hospitalization and was also assessed with a severity score (1 point/variable) calculated on the basis of fraction of inhaled O(2) ≥ 30%, hospitalization >5 days, and pediatric intensive care unit admission. RESULTS: hMPV was identified in 58 of 305 outpatient children (19.0%) and 69 of 734 hospitalized children (9.4%), second only to RSV (48.2% and 63.6%, respectively). In multivariate regression analysis of hMPV cases, age <6 months and household crowding were associated with hospitalization. Among hospitalized patients, risk factors for severe hMPV disease were female sex, prematurity, and genotype B infection. Age <6 months, comorbidities, and household crowding were risk factors for RSV hospitalization; breast-feeding and viral coinfection were protective. Age <6 months and prematurity were associated with severe RSV cases among hospitalized children. CONCLUSIONS: hMPV and RSV severity risk factors may differ slightly. These findings will inform hMPV prevention strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".