Remote Assessment of Real-World Surgical Safety Checklist Performance Using the OR Black Box: A Multi-Institutional Evaluation
Bibliographic record
Abstract
BACKGROUND: Large-scale evaluation of surgical safety checklist performance has been limited by the need for direct observation. The operating room (OR) Black Box is a multichannel surgical data capture platform that may allow for the holistic evaluation of checklist performance at scale. STUDY DESIGN: In this retrospective cohort study, data from 7 North American academic medical centers using the OR Black Box were collected between August 2020 and January 2022. All cases captured during this period were analyzed. Measures of checklist compliance, team engagement, and quality of checklist content review were investigated. RESULTS: Data from 7,243 surgical procedures were evaluated. A time-out was performed during most surgical procedures (98.4%, n = 7,127), whereas a debrief was performed during 62.3% (n = 4,510) of procedures. The mean percentage of OR staff who paused and participated during the time-out and debrief was 75.5% (SD 25.1%) and 54.6% (SD 36.4%), respectively. A team introduction (performed 42.6% of the time) was associated with more prompts completed (31.3% vs 18.7%, p < 0.001), a higher engagement score (0.90 vs 0.86, p < 0.001), and a higher percentage of team members who ceased other activities (80.3% vs 72%, p < 0.001) during the time-out. CONCLUSIONS: Remote assessment using OR Black Box data provides useful insight into surgical safety checklist performance. Many items included in the time-out and debrief were not routinely discussed. Completion of a team introduction was associated with improved time-out performance. There is potential to use OR Black Box metrics to improve intraoperative process measures.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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 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".