Relationship between intraoperative non-technical performance and technical events in bariatric surgery
Bibliographic record
Abstract
Abstract Background The operating theatre is a unique environment with complex team interactions, where technical and non-technical performance affect patient outcomes. The correlation between technical and non-technical performance, however, remains underinvestigated. The purpose of this study was to explore these interactions in the operating theatre. Methods A prospective single-centre observational study was conducted at a tertiary academic medical centre. One surgeon and three fellows participated as main operators. All patients who underwent a laparoscopic Roux-en-Y gastric bypass and had the procedures captured using the Operating Room Black Box® platform were included. Technical assessment was performed using the Objective Structured Assessment of Technical Skills and Generic Error Rating Tool instruments. For non-technical assessment, the Non-Technical Skills for Surgeons (NOTSS) and Scrub Practitioners' List of Intraoperative Non-Technical Skills (SPLINTS) tools were used. Spearman rank-order correlation and N-gram statistics were conducted. Results Fifty-six patients were included in the study and 90 procedural steps (gastrojejunostomy and jejunojejunostomy) were analysed. There was a moderate to strong correlation between technical adverse events (rs = 0·417–0·687), rectifications (rs = 0·380–0·768) and non-technical performance of the surgical and nursing teams (NOTSS and SPLINTS). N-gram statistics showed that after technical errors, events and prior rectifications, the staff surgeon and the scrub nurse exhibited the most positive non-technical behaviours, irrespective of operator (staff surgeon or fellow). Conclusion This study demonstrated that technical and non-technical performances are related, on both an individual and a team level. Valuable data can be obtained around intraoperative errors, events and rectifications.
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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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".