Assessing the impact of introducing the 'ventilator bundle' on outcomes for mechanically ventilated patients
Bibliographic record
Abstract
The concept of bundles was developed by the Institute for Healthcare Improvement. Individual bundle elements are built on evidence-based practice, and the bundle concept is that when these elements are executed together they produce better outcomes than in isolation. There is, however, limited evidence linking the use of bundles to demonstrable changes in patient outcomes. As a preliminary analysis to inform a multicentre evaluation, we explored the effect of the introduction of the 'ventilator bundle' on the outcomes for mechanically ventilated patients in a single critical care unit. Data were extracted for mechanically ventilated admissions from a single unit participating in the Case Mix Programme that was an early adopter of the ventilator bundle. A risk prediction model was developed using data from admissions during the 3.5 years prior to the introduction of the bundle and applied to admissions during the 3 years since introduction to estimate the cumulative excess mortality (observed minus expected deaths). There were 762 ventilated admissions prior to the introduction of the bundle and 618 since. The cumulative excess mortality plot suggested a reduction in mortality after introduction of the bundle (Figure 1 ) but this was not statistically significant (relative risk reduction 10.9%, 95% confidence interval -10.2% to 31.8%). Figure 1
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".