Reducing Pediatric Unplanned Extubation: A National Quality Improvement Collaborative
Bibliographic record
Abstract
OBJECTIVE: Unplanned extubation (UE) is a significant cause of harm for pediatric patients. Hospitals working with a quality improvement collaborative, Solutions for Patient Safety, tested and developed a UE bundle that demonstrated significant UE reduction after implementation. The objective of this study was to spread the UE bundle to a large number of children's hospitals using workgroups to facilitate bundle implementation for UE reduction. METHODS: Pediatric hospitals implemented the UE bundle in their neonatal, pediatric, and cardiac intensive care units and submitted data on their UE rate (UE number per ventilator days) and reliability to the bundle. Participating hospitals were divided into smaller workgroups that were used to identify barriers to bundle implementation, measurement, and maintenance. Workgroups were used to facilitate peer-to-peer discussion and sharing of resources, tools, and ideas. RESULTS: Eighty-three hospitals participated in workgroups between January 2020 and July 2023. During that time, the overall network rate of UE was reduced from 0.662 UE events per 100 ventilator days to 0.53 UE events per 100 ventilator days, representing a 19.9% reduction in UE events. After participating in workgroups, 53 hospitals (74%) experienced significant UE rate reductions or a significant increase in reliability to the bundle. Most hospitals maintained stable UE rates and reliability. Barriers to bundle implementation and auditing were identified and addressed in the workgroups. CONCLUSIONS: The use of workgroups was an effective method to facilitate bundle spread, support group learning, and provide resources to promote improvement efforts in a large improvement collaborative. Through structured improvement methods, children's hospitals have continued to decrease the rate of UE.
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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 | 0.041 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".