Improving weaning and liberation from mechanical ventilation for tracheostomy patients: a quality improvement initiative
Bibliographic record
Abstract
For patients in the intensive care unit (ICU), prolonged mechanical ventilation is associated with poor outcomes. A quality improvement (QI) initiative with the aim of reducing median time on the ventilator for tracheostomy patients was undertaken at a tertiary care ICU in Toronto, Canada. A QI team was formed, and using QI methodology, a deep understanding of our local process was achieved. Based on this information and on the latest evidence on weaning, a standard tracheostomy weaning protocol was designed. The protocol was refined through three developmental and two testing plan-do-study-act cycles. This study was a prospective time series showing the effect of the implementation of our intervention on tracheotomy patients' time on the ventilator. The baseline median number of days on the ventilator after tracheostomy insertion was 17. Within 12 months of the introduction of the intervention, a shift in the data showing a reduction in the median time on the ventilator to 10.6 days had developed. Length of stay in the ICU was reduced by 4.3 days. Adherence and compliance to the protocol also improved over time. A standard tracheostomy weaning protocol was successfully developed, tested and implemented in a tertiary care ICU. Using strategies such as frequent communication with key stakeholders and incorporating a tracheostomy weaning progress sheet to document and track tracheostomy patients and their outcomes, this QI intervention has become engrained in the local culture at our centre. This weaning protocol has successfully reduced the median time on the ventilator for tracheostomy patients by over 6 days.
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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 | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".