Characterising ‘<i>near miss</i>’ events in complex laparoscopic surgery through video analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Root cause analyses of surgical complications are of high importance to ensure surgical quality, but specific details on technical causes often remain unclear. Identifying subclinical intraoperative incidents attributable to technical errors is essential for developing rescue mechanisms to prevent adverse outcomes. OBJECTIVE: Descriptive study to characterise intraoperative technical error-event patterns in successful laparoscopic procedures. METHODS: Events (injuries) identified during prior blinded analyses of 54 unedited recordings of bariatric laparoscopic procedures were subjected to a secondary review to determine the presumed underlying error mechanism. The recordings were obtained from one university-based bariatric collaborative programme, and represented consultant, fellow and shared trainee cases. RESULTS: Sixty-six events were identified in 38 recordings, while 16 videos showed no events. In 25 (66%) of the videos that showed events, additional measures such as haemostasis or suture repair were required. Common identified events were minor bleeding (n=39, 59%), thermal injury to non-target tissue (n=7, 11%), serosal tears (n=6, 9%). Common error mechanisms were 'inadequate use of force/distance (too much)' (n=20, 30%) and 'inadequate visualisation' during grasping/dissecting (n=6, 9%), 'inadequate use of force/distance (too much)' using an energy device (n=6, 9%), or during suturing (n=6, 9%). All events were recognised intraoperatively. CONCLUSIONS: Analysis of successful operations allowed the identification of numerous error-event sequences. Reviewing injury mechanisms can enhance surgeons' understanding of relevant errors. This error awareness may aid surgeons in preparing for cases, help avoid errors and mitigate their consequences. Thus, this approach may impact future surgical education and quality initiatives aimed at reducing surgical risks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it