Implementation of Critical Care Response Teams in Ontario
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether introduction of CCRTs reduced mortality rates among patients who developed a postoperative complication, also referred to as FTR. BACKGROUND: CCRTs were introduced to improve patients' postoperative outcomes. Its effect on FTR continues to be actively investigated. METHODS: We conducted a population-based retrospective cohort study using administrative data from Ontario, Canada. We identified 810,279 patients admitted to hospital for major surgical procedures between January 2004 and December 2014, with a washout period consisting of the 9 months before and after the implementation of CCRTs in January 2007. Difference-in-differences analysis among patients who developed a postoperative complication (n = 148,882) was used to estimate the association between CCRT implementation and FTR before and after CCRT implementation in hospitals that did - versus did not - implement CCRT during the study period. RESULTS: A total of 810,279 patients were included, of whom 148,882 (18.4%) developed a postoperative surgical complication. Among patients who developed a postoperative complication, the overall proportion of FTR was 9.2% (n = 13,659). Among patients in hospitals that introduced CCRT, the RR of FTR was 0.84, [95% confidence interval (CI) 0.78-0.90] after implementation of CCRT, while over the same time period, the RR was 0.85 (95% CI 0.80-0.91) in hospitals that did not implement CCRT. The RR ratio (difference-indifferences) was 0.99 (95% CI 0.89-1.09). Among patients undergoing orthopedic surgery, the RR ratio was 0.84 (95% CI 0.75-0.95). CONCLUSION: Although implementation of CCRTs in hospitals in Ontario, Canada, did not reduce FTR among all surgical patients having surgery, CCRTs may reduce the risk of FTR among patients having orthopedic surgery.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".