What do central venous catheter-associated bloodstream infections have to do with bundles?
Bibliographic record
Abstract
Interest in the patient safety agenda continues to grow in North America. In the United States (US), the Institute for Healthcare Improvement (IHI) has begun a campaign to make health care safer and more effective by encouraging hospitals to implement interventions they believe can avoid 100,000 deaths between January 2005 and July 2006 (1). The IHI, a not-for-profit organization founded in 1991, promotes the improvement of health by advancing the quality and value of health care (2). Three of the six areas for action chosen by the IHI for their '100,000 Lives Campaign' relate to prevention of nosocomial infections: central line infections, surgical site infections and ventilator-associated pneumonia. In Canada, a grassroots patient safety campaign modelled after the IHI's '100,000 Lives Campaign' has formed (3). This 'Safer Healthcare Now!' campaign focuses on the same six strategies chosen for the '100,000 Lives Campaign'. Across the country, hospitals are being invited to join the 'Safer Healthcare Now!' campaign.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".