Relationship between intraoperative non-technical performance and technical events in bariatric surgery
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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