First-year Analysis of the Operating Room Black Box Study
Bibliographic record
Abstract
OBJECTIVE: To characterize intraoperative errors, events, and distractions, and measure technical skills of surgeons in minimally invasive surgery practice. BACKGROUND: Adverse events in the operating room (OR) are common contributors of morbidity and mortality in surgical patients. Adverse events often occur due to deviations in performance and environmental factors. Although comprehensive intraoperative data analysis and transparent disclosure have been advocated to better understand how to improve surgical safety, they have rarely been done. METHODS: We conducted a prospective cohort study in 132 consecutive patients undergoing elective laparoscopic general surgery at an academic hospital during the first year after the definite implementation of a multiport data capture system called the OR Black Box to identify intraoperative errors, events, and distractions. Expert analysts characterized intraoperative distractions, errors, and events, and measured trainee involvement as main operator. Technical skills were compared, crude and risk-adjusted, among the attending surgeon and trainees. RESULTS: Auditory distractions occurred a median of 138 times per case [interquartile range (IQR) 96-190]. At least 1 cognitive distraction appeared in 84 cases (64%). Medians of 20 errors (IQR 14-36) and 8 events (IQR 4-12) were identified per case. Both errors and events occurred often in dissection and reconstruction phases of operation. Technical skills of residents were lower than those of the attending surgeon (P = 0.015). CONCLUSIONS: During elective laparoscopic operations, frequent intraoperative errors and events, variation in surgeons' technical skills, and a high amount of environmental distractions were identified using the OR Black Box.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".