Association between implementation of an intensivist-led medical emergency team and mortality
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of implementation of a dedicated intensivist-led medical emergency team (IL-MET) on mortality in patients admitted to the intensive care unit (ICU). METHODS: All adult ward admissions to the ICU between July 2002 and December 2009 were reviewed (n=1920) after excluding readmissions and admissions for <24 h. IL-MET hours were defined as 8:00-15:59 (Monday to Friday). The following periods were analysed: period 1: 1 July 2002-31 August 2004 (control); period 2: 1 September 2004-11 February 2007 (partial MET without dedicated intensivist); and period 3: 12 February 2007-31 December 2009 (hospital-wide IL-MET). RESULTS: During all three periods, there were no significant differences in length of stay or mortality (IL-MET vs non-IL-MET hours, p>0.1 for all). On multivariate analysis, Acute Physiology and Chronic Health Evaluation (APACHE) II score and age were independently associated with mortality in all three periods (p<0.05 for all). During period 3, there was a non-significant trend towards decreased mortality if admitted during IL-MET hours (OR 0.73, 95% CI 0.51 to 1.03, p=0.08). During period 3, there was a non-significant trend towards decreased mortality if admitted during IL-MET hours (OR 0.73, 95% CI 0.51 to 1.03, p=0.08). However, this result likely reflects the observed increase in mortality during non-IL MET hours rather than improved mortality during IL-MET hours. CONCLUSION In a single centre experience, implementation of an IL-MET did not reduce the rate of in-hospital death or lengths of stay.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".