Intraoperative injuries from abdominopelvic surgery: an analysis of national medicolegal data
Bibliographic record
Abstract
Background: Intraoperative injuries during abdominopelvic surgery can be associated with substantial patient harm. The objective of this study was to describe abdominopelvic intraoperative injuries and their contributing factors among medicolegal cases. Methods: This study was a descriptive analysis of medicolegal matters reported to a national body, with subgroup analyses by type of surgery. We reviewed medicolegal matters involving a population-based sample of physicians who were subject to a civil legal action or complaint to a regulatory authority that was closed between 2013 and 2017 in Canada. Results: Our analysis included 181 civil legal cases and 88 complaints to a regulatory authority. Among legal cases, 155 patients (85.6%) (median age 47 yr) underwent elective procedures. The most common injury site was the bowel (53 cases [29.3%]). Injuries frequently occurred during dissection (79 [43.6%]) and ligation (38 [21.0%]), were identified postoperatively (138 [76.2%]) and necessitated further surgery (139 [76.8%]). Many patients experienced severe harm (55 [30.4%]) or died (25 [13.8%]). Peer experts in nongynecologic cases were more likely than those in gynecologic cases to include criticisms of a provider in a harmful incident (79 [71.2%] v. 30 [42.9%], p < 0.01). Peer expert criticisms often related to clinical evaluation, decision-making and misidentification of anatomy. Criticisms of nontechnical skills identified documentation and communication deficiencies. Conclusion: This study confirms the importance of provider and team training to improve clinical evaluation and decision-making, documentation and communication. Effective protocols may help support clinicians in providing safer surgical care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".