Effect of the surgical safety checklist on provider and patient outcomes: a systematic review
Bibliographic record
Abstract
Background Despite being implemented for over a decade, literature describing how the surgical safety checklist (SSC) is completed by operating room (OR) teams and how this relates to its effectiveness is scarce. This systematic review aimed to: (1) quantify how many studies reported SSC completion versus described how the SSC was completed; (2) evaluate the impact of the SSC on provider outcomes ( C ommunication, case U nderstanding, S afety C ulture, CUSC), patient outcomes (complications, mortality rates) and moderators of these relationships. Methods A systematic literature search was conducted using Medline, CINAHL, Embase, PsycINFO, PubMed, Scopus and Web of Science on 10 January 2020. We included providers who treat human patients and completed any type of SSC in any OR or simulation centre. Statistical directional findings were extracted for provider and patient outcomes and key factors (eg, attentiveness) were used to determine moderating effects. Results 300 studies were included in the analysis comprising over 7 302 674 operations and 2 480 748 providers and patients. Thirty-eight per cent of studies provided at least some description of how the SSC was completed. Of the studies that described SSC completion, a clearer positive relationship was observed concerning the SSC’s influence on provider outcomes (CUSC) compared with patient outcomes (complications and mortality), as well as related moderators. Conclusion There is a scarcity of research that examines how the SSC is completed and how this influences safety outcomes. Examining how a checklist is completed is critical for understanding why the checklist is successful in some instances and not others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".