Impact of surgical checklist and its completion on complications and mortality in urgent colorectal procedures
Bibliographic record
Abstract
OBJECTIVE: to assess the impact of using a surgical checklist and its completion on complications such as surgical site infection (SSI), reoperation, readmission, and mortality in patients subjected to urgent colorectal procedures, as well as the reasons for non adherence to this instrument in this scenario, in a university hospital in Ottawa, Canada. METHODS: this is a retrospective, epidemiological study. We collected data from an electronic database containing information on patients undergoing urgent colorectal operations, and analyzed the occurrence of SSI, reoperation, readmission, and death in a 30 day period, as well as the completion of the checklist. We conducted a descriptive statistical analysis and logistic regression. RESULTS: we included 5,145 records, of which 5,083 (98.8%) had complete checklists. As for the outcomes evaluated, cases with complete checklists displayed higher SSI rate, 9.1% vs. 6.5% (p=0.466), lower reoperation rate, 5% vs.11.3% (p=0.023), lower readmission rates, 7.2% vs. 11.3% (p=0.209), and lower mortality, 3.0% vs. 6.5% (p=0.108) than cases with incomplete ones. CONCLUSION: there was a high level of checklist completion and a larger number of the outcomes in the reduced percentage of incomplete checklists found, demonstrating the impact of its utilization on the safety of patients undergoing urgent operations.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".