Seasonality Patterns in Croup Presentations to Emergency Departments in Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Croup is a common pediatric respiratory illness presenting to the emergency department (ED) in the fall and winter months. Most cases are caused by parainfluenza viruses. We examine the monthly patterns of young children who made croup-related visits to EDs in Alberta, Canada. METHODS: Emergency department visits were identified in provincial administrative databases to obtain all ED encounters for croup made by young children (aged ≤2 years) during 6 years (April 1, 1999, to March 30, 2005). Time series models (seasonal autoregressive integrated moving average) were developed to capture temporal and seasonal trends and predict future presentations. RESULTS: Overall, 27,355 croup-related ED visits were made during the study period. More males (62%) than females presented, and most (43%) were younger than 1 year. Differences were observed in the number of visits made in odd and even years. Peak visits occurred in November for odd years and in February for other years. Strong seasonal patterns at 12 months were detected and included in the modeling. CONCLUSIONS: We observed the presence of a clear biennial pattern of croup ED visits. The seasonal autoregressive moving average models and predictions offer insights into the epidemiology of croup-related visits to EDs and may be helpful in planning both research and resource needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".