Multicenter Quality Collaborative to Reduce Overuse of High-Flow Nasal Cannula in Bronchiolitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: High-flow nasal cannula (HFNC) for bronchiolitis increased over the past decade without clear benefit. This quality improvement collaborative aimed to reduce HFNC initiation and treatment duration by 30% from baseline. METHODS: Participating hospitals either reduced HFNC initiation (Pause) or treatment duration (Holiday) in patients aged <24 months admitted for bronchiolitis. Participants received either Pause or Holiday toolkits, including: intervention protocol, training/educational materials, electronic medical record queries for data acquisition, small-group coaching, webinars, and real-time access to run charts. Pause arm primary outcome was proportion of patients initiated on HFNC. Holiday arm primary outcome was geometric mean HFNC treatment duration. Length of stay (LOS) was balancing measure for both. Each arm served as contemporaneous controls for the other. Outcomes analyzed using interrupted time series (ITS) and linear mixed-effects regression. RESULTS: Seventy-one hospitals participated, 30 in the Pause (5746 patients) and 41 in the Holiday (7903 patients). Pause arm unadjusted HFNC initiation decreased 32% without LOS change. ITS showed immediate 16% decrease in initiation (95% confidence interval [CI] -27% to -5%). Compared with contemporaneous controls, Pause hospitals reduced HFNC initiation by 23% (95% CI -35% to -10%). Holiday arm unadjusted HFNC duration decreased 28% without LOS change. ITS showed immediate 11.8 hour decrease in duration (95% CI -18.3 hours to -5.2 hours). Compared with contemporaneous controls, Holiday hospitals reduced duration by 11 hours (95% CI -20.7 hours to -1.3 hours). CONCLUSIONS: This quality improvement collaborative reduced HFNC initiation and duration without LOS increase. Contemporaneous control analysis supports intervention effects rather than secular trends toward less use.
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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 | 0.065 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".