Bronchiolitis severity and future healthcare utilization in healthy term infants
Bibliographic record
Abstract
Introduction: Does bronchiolitis illness severity impact future healthcare utilization in young children? Aims: To determine differences in subsequent healthcare use in healthy, term children based on severity of initial episode. Methods: A retrospective cohort study compared healthcare utilization over 5 years after an index bronchiolitis hospital visit in healthy, term children <12 months of age discharged from hospitals in Calgary, Canada, Jan 1, 2010 – May 31, 2015. Patient information from electronic medical records. There were 3 exposure groups: emergency department (ED) only (mild illness); inpatient admission (moderate); pediatric intensive care unit (PICU) admission (severe) with bronchiolitis. Subsequent healthcare utilization was outpatient physician visits, ED visits and hospitalizations. Results: 2441 children had bronchiolitis. 1659 (68%) mild, 721 (29.5%) moderate, 61 (2.5%) severe (PICU admission). Those with severe illness had the highest rates of physician outpatient visits in the first year (median = 8) compared to those with moderate (median = 7, p = 0.02) and mild illness (median = 6, p <0.001). By year 5 there was no difference between the number of physician outpatient visits between groups (p = 0.71). Subsequent hospitalizations were higher in the moderate compared to the mild group during the first two years after diagnosis (first year, p < 0.001; second year, p = 0.018) but this difference resolved in the following years. Rates of ED visits did not differ across groups at any period. Relevance: Subsequent healthcare utilization in healthy, term children <12 months of age with mild, moderate or severe bronchiolitis is not meaningfully different by 5 years after initial illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".