Improving Adherence to a Mechanical Ventilation Weaning Protocol for Critically Ill Adults: Outcomes After an Implementation Program
Bibliographic record
Abstract
BACKGROUND: Despite multiple reminders, education sessions, and multidisciplinary team involvement, adherence to an evidence-based mechanical ventilation weaning protocol had been less than 1% in a general systems intensive care unit since implementation. OBJECTIVE: To assess the effectiveness of using an implementation program, the Model for Accelerating Improvement, to improve adherence and clinical outcomes after restarting a mechanical ventilation weaning protocol in an adult general systems intensive care unit. METHODS: A prospective comparative design, before and after implementation of the Model for Accelerating Improvement, was used with a consecutive sample of 129 patients and 112 multidisciplinary team members. Clinical outcomes were rate of unsuccessful extubations, rate of ventilator-associated pneumonia, and duration of mechanical ventilation; practice outcomes were staff's understanding of the mechanical ventilation weaning protocol, perceptions of the practice safety climate, and adherence to the weaning protocol. RESULTS: After the intervention, the rate of unsuccessful extubations decreased, and staff's understanding of and adherence to the weaning protocol increased significantly. The rate of ventilator-associated pneumonia, duration of mechanical ventilation, and staff's perceptions of the practice safety climate did not change significantly. CONCLUSION: Implementing the Model for Accelerating Improvement improved understanding of and adherence to protocol-directed weaning and reduced the rate of unsuccessful extubations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".