Implementation of Critical Care Response Teams in Ontario. Impact on the Outcomes of Surgical Patients
Bibliographic record
Abstract
Critical care response teams (CCRTs) are thought to improve patients’ outcomes by attending to patient clinical deterioration earlier. Failure to rescue is defined as the risk of death among patients who had a surgical complication. CCRTs were implemented in the province of Ontario in 2006. The aim of this study was to 1) evaluate, in surgical adult patients in Ontario who develop a complication, if CCRTs decreases mortality rates (decrease in FTR) by accounting to secular trends and 2) if this decrease in FTR is different according to the type of surgical procedure. A comparison of the risk of FTR before and after the implementation of CCRTs was performed between centers that implemented CCRTs and those that did not. A difference-in-difference analysis was performed to estimate the association between the implementation of CCRTs and FTR. A total of 810,279 surgical patients were included in the study, of whom 148,882 developed a surgical complication and comprised the study population. Among all surgical patients the risk ratio for FTR was 0.99 (0.89-1.09). Among patients undergoing an orthopedic intervention, the risk ratio was 0.84 (0.75-0.95). While implementation of CCRTs did not reduce the risk of FTR among all surgical patients in Ontario, patients who had orthopedic surgery may appeared to benefit most.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".