Anchoring the sign-out phase of the Surgical Safety Checklist to emergence from anesthesia: a proof-of-concept quality-improvement study
Bibliographic record
Abstract
BACKGROUND: The Surgical Safety Checklist (SSC) is a communication tool used to improve patient safety and teamwork within operating rooms. Unlike the sign-in and timeout phases, the timing for completion of the sign-out phase is ambiguous, lacks a clear and definitive clinical anchor on when to be performed, and fails to capture important safety data related to the patient's emergence from anesthesia, wherein the risks of complications are greatest. We sought to assess perceptions of operating room team members on whether emergence from anesthesia is an appropriate clinical anchor to conduct the SSC sign-out phase. METHODS: In this single-centre proof-of-concept quality-improvement study, the sign-out phase of the SSC was performed following patient emergence from anesthesia. Operating room team members from surgery, anesthesiology, and nursing were approached to complete a self-administered questionnaire. Participants were asked whether, compared with routine sign-out performance, performing the sign-out phase following emergence from anesthesia maximized patient safety, compliance, communication, team member availability, and quality improvement. Responses were graded on a 5-point Likert scale. RESULTS: Eighty-two operating room team members participated in our study. After experiencing the intervention, most participants agreed or strongly agreed that performing the sign-out phase following emergence from anesthesia maximized patient safety (70.7%), compliance (67.1%), communication (75.6%), and quality improvement (67.0%). More than half agreed that performing the sign-out following emergence from anesthesia maximized team member availability (59.8%). CONCLUSION: This proof-of-concept quality-improvement study suggests that emergence from anesthesia is an appropriate clinical anchor for the time to perform the SSC sign-out phase.
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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 | 0.105 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".