The OR Black Box as a Novel Tool to Improve Surgical Safety and Education
Bibliographic record
Abstract
Errors are prevalent in medicine and frequently lead to increased morbidity and mortality for patients. The complex environment within the operating room, and the multiple people and teams involved in providing patient care, make surgery especially prone to error. Healthcare relies on incident reports, morbidity and mortality (M and M) rounds, and review of patient charts to retrospectively determine factors that contributed to severe errors or near misses.1 Unfortunately, these methods focus primarily on incidents that result in significant patient harm and are subject to recall bias and poor capture of details surrounding key factors or events. Often, many seemingly minor contributions or incidents that do not lead to harm are deemed irrelevant and are not adequately assessed or are omitted altogether. The OR Black Box is a novel system of cameras, monitors, and audio recorders that captures everything that happens in the operating room to allow for future assessment of all errors that occur during a case. Such capture and assessment enable surgeons to review all intraoperative errors and determine what factors lead to errors so they can be avoided in future. The OR Black Box can also be used as an educational tool to facilitate surgical trainee feedback and review of surgical skills. Routine and widespread use of the OR Black Box has the potential to improve surgical safety and training and is a promising new tool for healthcare advancement.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".