Respiratory Viruses and Treatment Failure in Children With Asthma Exacerbation
Bibliographic record
Abstract
OBJECTIVES: Respiratory pathogens commonly trigger pediatric asthma exacerbations, but their impact on severity and treatment response remains unclear. METHODS: We performed a secondary analysis of the Determinants of Oral Corticosteroid Responsiveness in Wheezing Asthmatic Youth (DOORWAY) study, a prospective cohort study of children (aged 1-17 years) presenting to the emergency department with moderate or severe exacerbations. Nasopharyngeal specimens were analyzed by RT-PCR for 27 respiratory pathogens. We investigated the association between pathogens and both exacerbation severity (assessed with the Pediatric Respiratory Assessment Measure) and treatment failure (hospital admission, emergency department stay >8 hours, or relapse) of a standardized severity-specific treatment. Logistic multivariate regressions were used to estimate average marginal effects (absolute risks and risk differences [RD]). RESULTS: Of 958 participants, 61.7% were positive for ≥1 pathogen (rhinovirus was the most prevalent [29.4%]) and 16.9% experienced treatment failure. The presence of any pathogen was not associated with higher baseline severity but with a higher risk of treatment failure (20.7% vs 12.5%; RD = 8.2% [95% confidence interval: 3.3% to 13.1%]) compared to the absence of a pathogen. Nonrhinovirus pathogens were associated with an increased absolute risk (RD) of treatment failure by 13.1% (95% confidence interval: 6.4% to 19.8%), specifically, by 8.8% for respiratory syncytial virus, 24.9% for influenza, and 34.1% for parainfluenza. CONCLUSIONS: Although respiratory pathogens were not associated with higher severity on presentation, they were associated with increased treatment failure risk, particularly in the presence of respiratory syncytial virus, influenza, and parainfluenza. This supports influenza prevention in asthmatic children, consideration of pathogen identification on presentation, and exploration of treatment intensification for infected patients at higher risk of treatment failure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".