Using the Operating Room Black Box to Assess Surgical Team Member Adaptation Under Uncertainty
Bibliographic record
Abstract
OBJECTIVE: Identify how surgical team members uniquely contribute to teamwork and adapt their teamwork skills during instances of uncertainty. BACKGROUND: The importance of surgical teamwork in preventing patient harm is well documented. Yet, little is known about how key roles (nurse, anesthesiologist, surgeon, and medical trainee) uniquely contribute to teamwork during instances of uncertainty, particularly when adapting to and rectifying an intraoperative adverse event (IAE). METHODS: Audiovisual data of 23 laparoscopic cases from a large community teaching hospital were prospectively captured using OR Black Box. Human factors researchers retrospectively coded videos for teamwork skills (backup behavior, coordination, psychological safety, situation assessment, team decision-making, and leadership) by team role under 2 conditions of uncertainty: associated with an IAE versus no IAE. Surgeons identified IAEs. RESULTS: In all, 1015 instances of teamwork skills were observed. Nurses adapted to IAEs by expressing more backup behavior skills (5.3× increase; 13.9 instances/hour during an IAE vs 2.2 instances/hour when no IAE) while surgeons and medical trainees expressed more psychological safety skills (surgeons: 3.6× increase; 30.0 instances/hour vs 6.6 instances/hour and trainees: 6.6× increase; 31.2 instances/hour vs 4.1 instances/hour). All roles expressed fewer situation assessment skills during an IAE versus no IAE. CONCLUSIONS: OR Black Box enabled the assessment of critically important details about how team members uniquely contribute during instances of uncertainty. Some teamwork skills were amplified, while others dampened when dealing with IAEs. The knowledge of how each role contributes to teamwork and adapts to IAEs should be used to inform the design of tailored interventions to strengthen interprofessional teamwork.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".