Assessing endovascular team performances in a hybrid room using the Black Box system: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The hybrid room (HR) is a complex, high-risk environment, requiring teams (surgeons, anesthesiologists, nurses, technologists) to master various skills, including the 'As Low As Reasonably Achievable' principle of radiation safety. This prospective single center cohort reports the first use of the Operating Room Black Box (ORBB) in a HR. This medical data recording system captures procedural and audio-visual data to facilitate structured team performance analysis. METHODS: Patients planned for endovascular repair of an infrarenal abdominal aortic aneurysm (EVAR) or treatment of symptomatic iliac-femoral-popliteal atherosclerotic disease (Peripheral Vascular Interventions or PVI) were included. Validated measures and established assessment tools were used to assess (non-)technical skills, radiation safety performance and environmental distractions. RESULTS: Six EVAR and sixteen PVI procedures were captured. Technical performance for one EVAR was rated 19/35 on the procedure-specific scale, below the 'acceptable' score of 21. Technical skills were rated above acceptable in all PVI procedures. Shared decision making and leadership were rated highly in 12/22 cases, whereas surgical communication and nurses' task management were rated low in 14/22 cases. Team members rarely stepped back from the C-arm during digital subtraction angiography. Radiation safety behavior was scored below 'acceptable' in 14/22 cases. A median (interquartile range) number of 12 (6-23) auditory distractions was observed per procedure. CONCLUSIONS: The ORBB facilitates holistic workplace-based assessment of endovascular performance in a HR by combining objective assessment parameters and rating scale-based evaluations. Strengths and weaknesses were identified in team members' (non-)technical and radiation safety practices. This technology has the potential to improve vascular surgical practice, though human input remains crucial. (NCT04854278).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".