Prevention of Ventilator-Associated Pneumonia in the Calgary Health Region: A Canadian Success Story!
Bibliographic record
Abstract
V entilator-associated pneumonia (VAP) is a leading cause of morbidity and mortality among hospitalized patients.VAP develops in 10-20% of mechanically ventilated patients, with those acquiring VAP experiencing greater attributable mortality and longer lengths of stay in intensive care units (ICUs) (Keith et al. 2004;Safdar et al. 2005; US Centers for Disease Control and Prevention 2005).The Calgary Health Region (CHR) provides healthcare services to 1.2 million residents in Southern Alberta and tertiary services for 1.3 million residents of Alberta and British Columbia.The Department of Critical Care Medicine has three adult multi-system ICUs, admitting over 3,000 patients per year to 38 ICU beds.In recent years, our infection control-based VAP surveillance system discovered a significant incidence of VAP in our regional ICUs.From 1998 to 2002, CHR's rate of VAP was 19 cases per 1,000 ventilator-days.Paralleling published observations from other centres, patients acquiring VAP in the CHR had significantly longer ICU stays, contributing to suboptimal resource use.Accordingly, the department elected to focus on the prevention of VAP.This focus began in 2002 in conjunction with our participation in the Institute for Healthcare Improvement's Project Impact, with a focus on the ventilator bundle.Joining the Canadian Collaborative on Improving Patient Care and Safety in the ICU (www.improvementassociates.com) in 2004 allowed us to further benefit from the sharing of practice and experience by introducing additional change concepts including the VAP bundle.This "ideas at work" case study is unique in that it provides a Canadian context, makes use of a modified bundle focused exclusively on measures linked to the prevention of VAP and exemplifies strategies for VAP prevention as applied across a health region rather than an individual hospital.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".