Recommendations for surgical safety checklist use in Canadian children’s hospitals
Bibliographic record
Abstract
BACKGROUND: There is ample evidence that avoidable harm occurs in patients, including children, who undergo surgical procedures. Among a number of harm mitigation strategies, the use of surgical safety checklists (SSC) is now a required organizational practice for accreditation in all North American hospitals. Although much has been written about the effects of SSC on outcomes of adult surgical patients, there is a paucity of literature on the use and role of the SSC as an enabler of safe surgery for children. METHODS: The Pediatric Surgical Chiefs of Canada (PSCC) advocates on behalf of all Canadian children undergoing surgical procedures. We undertook a survey of the use of SSC in Canadian children's hospitals to understand the variability of implementation of the SSC and understand its role as both a measure and driver of patient safety and to make specific recommendations (based on survey results and evidence) for standardized use of the SSC in Canadian children's hospitals. RESULTS: Survey responses were received from all 15 children's hospitals and demonstrated significant variability in how the checklist is executed, how compliance is measured and reported, and whether or not use of the checklist resulted in specific instances of error prevention over a 12-month observation period. There was near unanimous agreement that use of the SSC contributed positively to the safety culture of the operating room. CONCLUSION: Based on the survey results, the PSCC have made 5 recommendations regarding the use of the SSC in Canadian children's hospitals.
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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 | 0.021 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".