Modifications of the World Health Organization’s Surgical Safety Checklist—Ways Forward to Ensure Sustainable Implementation
Bibliographic record
Abstract
The study by Brindle and colleagues 1 elsewhere in JAMA Network Open provides novel international perspectives on modifications of the World Health Organization's (WHO) Surgical Safety Checklist (SSC). The article describes opportunities for increasing team members' involvement and ownership through modifications of the checklist. In their qualitative study, semistructured interviews of 51 clinicians and hospital administrators were conducted across 5 high-income countries: Australia, Canada, New Zealand, the United States, and the United Kingdom. Five themes emerged from the data: awareness and involvement in SSC modifications; reasons for modifications; types of modifications; the impact of modifications; and perceived barriers to SSC modifications. Importantly, these findings address contemporary issues in anesthesia and surgery and may hopefully contribute to reinvigorating the SSC as a dynamic and relevant tool for surgical patients' safety.
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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 | 0.269 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".