Emergency department visits and hospitalisations for asthma, COPD and respiratory tract infections: what is the role of respiratory viruses, and return to school in September, January and March?
Bibliographic record
Abstract
BACKGROUND: Asthma exacerbations increase in September coinciding with children returning to school. The aim of this study was to investigate whether this occurs 1) for COPD and respiratory tract infections (RTIs); 2) after school resumes in January and March; and 3) identify which viruses may be responsible. METHODS: Emergency department (ED) visits and admissions for asthma, COPD and RTIs and the prevalence of viruses in Ontario, Canada were analysed daily between 2003 and 2013. ED visits and admissions were provided by the Canadian Institute for Health Information. Viral prevalence was obtained from the Centre for Immunisation and Respiratory Infectious Diseases. RESULTS: ED visits and admissions rates demonstrated a biphasic pattern. Lowest rates occurred in July and August and the highest rates in September for asthma, and after December for COPD and RTI. The increase in rates for 30 days before and after school return in September was greatest for children with asthma <15 years (2.4-2.6×). Event rates fell after school return in January for all three conditions ranging from 10-25%, and no change followed March break for asthma and COPD. Human rhinovirus was prevalent in summer with a modest relationship to asthma rates in September. The prevalence of respiratory syncytial virus, influenza A and coronavirus was associated with sustained event rates for COPD and RTIs. CONCLUSIONS: Asthma, COPD and RTIs increase in September but do not occur after return to school in January and March. Human rhinovirus is associated with ED visits and admissions only in September.
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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 | 0.001 | 0.001 |
| 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".