Failure to rescue in emergency general surgery in Canada
Bibliographic record
Abstract
BACKGROUND: The risk of death after a postoperative complication - known as failure to rescue (FTR) - has been proposed to be superior to traditional benchmarking outcomes, such as complication and mortality rates, as a measure of system quality. The purpose of this study was to identify the current FTR rate in emergency general surgery (EGS) centres across Canada. We hypothesized that substantial variability exists in FTR rates across centres. METHODS: In this multicentre retrospective cohort study, we performed a secondary analysis of data from a previous study designed to evaluate operative intervention for nonappendiceal, nonbiliary disease by 6 EGS services across Canada (1 in British Columbia, 1 in Alberta, 3 in Ontario and 1 in Nova Scotia). Patients underwent surgery between Jan. 1 and Dec. 31, 2014. We conducted univariate analyses to compare patients with and without complications. We performed a sensitivity analysis examining the mortality rate after serious complications (Clavien-Dindo score 3 or 4) that required a surgical intervention or specialized care (e.g., admission to intensive care unit). RESULTS: A total of 2595 patients were included in the study cohort. Of the 206 patients who died within 30 days, 145 (70.4%) experienced a complication before their death. Overall, the mortality rate after any surgical complication (i.e., FTR) was 16.0%. Ranking of sites by the traditional outcomes of complication and mortality rates differed from the ranking when FTR rate was included in the assessment. CONCLUSION: There was variability in FTR rates across EGS services in Canada, which suggests that there is opportunity for ongoing quality-improvement efforts. This study provides FTR benchmarking data for Canadian EGS services.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".