Mapping Distractions in the Hybrid Operating Room During Elective Endovascular Aortic Procedures
Bibliographic record
Abstract
BACKGROUND: The hybrid operating room (OR) is a complex environment where numerous auditory and visual stimuli are encountered, potentially affecting team performance and postoperative outcomes. This study aimed to quantify distractions during elective endovascular aortic procedures in a hybrid OR using audiovisual data collected with a medical data recorder. METHODS: This retrospective, observational, single-center study analyzed elective endovascular procedures for aneurysmal or occlusive atherosclerotic disease in a hybrid OR using the OR Black Box (Surgical Safety Technologies Inc., Toronto, Canada). Distractions were characterized using a modified Disruptions in Surgery Index. Descriptive and nonparametric statistics were used to describe the number of distractions per procedural phase. Associations of distractions with total surgical time and observed number of healthcare workers present in the OR were examined. RESULTS: Twenty-two endovascular procedures were analyzed with good to excellent interrater (ICC 0.86) and intrarater (ICC 0.89, 0.96) reliability. Median surgical time was 110 min (IQR 73-138). Distractions were observed at a median rate of 81 per hour (IQR 67-94), with internal traffic being most frequent (36 per hour; IQR 31-46). Significantly more distractions occurred during the closing phase (p < 0.001). Total surgical time and number of healthcare workers were not associated with the number of distractions per hour. CONCLUSIONS: Distractions occur frequently in the hybrid OR and can be mapped with a medical data recorder. Further research is needed to unravel the impact of distractions on clinical outcomes and to evaluate quality improvement initiatives to reduce distractions during surgery.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".