Impact of the WHO Surgical Safety Checklist Relative to Its Design and Intended Use: A Systematic Review and Meta-Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to identify what parts of the World Health Organization Surgical Safety Checklist (WHO SSC) are working, what can be done to make it more effective, and to determine if it achieved its intended effect relative to its design and intended use. STUDY DESIGN: We conducted a qualitative thematic analysis and meta-meta-analyses of findings in WHO SSC systematic reviews following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. RESULTS: Twenty systematic reviews were included for qualitative thematic analysis. Narrative information was coded in 4 primary areas with a focus on impact of the WHO SSC. Four themes-Clinical Outcomes, Process Measures, Team Dynamics and Communication, and Safety Culture-pertained directly to the aims or purposes behind the development of the SSC. The other 2 themes-Efficiency and Workload involved in using the checklist and Checklist Impact on Institutional Practices-are associated with SSC use, but were not focal areas considered during its development. Included in the 20 systematic reviews were 24 unique observational cohort studies that reported pre-post data on a total of 18 clinical outcomes. Mortality, morbidity, surgical site infection, pneumonia, unplanned return to the operating room, urinary tract infection, blood loss requiring transfusion, unplanned intubation, and sepsis favored the use of the WHO SSC. Deep vein thrombosis was the only postoperative outcome assessed that did not favor use of the WHO SSC. CONCLUSIONS: The WHO SSC positively impacts the things it was explicitly designed to address and does not positively impact things it was not explicitly designed for.
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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 | 0.055 | 0.129 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.053 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".