Effect of surgical safety checklists on pediatric surgical complications in Ontario
Bibliographic record
Abstract
BACKGROUND: In health care, most preventable adverse events occur in the operating room. Surgical safety checklists have become a standard of care for safe operating room practice, but there is conflicting evidence for the effectiveness of checklists to improve perioperative outcomes in some populations. Our objective was to determine whether surgical safety checklists are associated with a reduction in the proportion of children who had perioperative complications. METHODS: We conducted a retrospective cohort study using administrative health care databases housed at the Institute for Clinical Evaluative Sciences to compare the risk of perioperative complications in children undergoing common types of surgery before and after the mandated implementation of surgical safety checklists in 116 acute care hospitals in Ontario. The primary outcome was a composite outcome of 30-day all-cause mortality and perioperative complications. RESULTS: We identified 14 458 and 14 314 surgical procedures in pre- and postchecklist groups, respectively. The proportion of children who had perioperative complications was 4.08% (95% confidence interval [CI] 3.76%-4.40%) before the implementation of the checklist and 4.12% (95% CI 3.80%-4.45%) after implementation. After we adjusted for confounding factors, we found no significant difference in the odds of perioperative complications after the introduction of surgical safety checklists (adjusted odds ratio 1.01, 95% CI 0.90-1.14, p = 0.9). INTERPRETATION: The implementation of surgical safety checklists for pediatric surgery in Ontario was not associated with a reduction in the proportion of children who had perioperative complications. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT02419053.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".