Prevention and management of urologic injury during gynecologic laparoscopy
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article provides an update on the best practices for the prevention, recognition, and management of urinary tract injuries that may occur during gynecologic laparoscopic surgery. RECENT FINDINGS: Higher surgical volume is directly associated with improved surgical outcomes, denoted by consistently lower rates of complications for commonplace procedures such as hysterectomy. As a result, expert opinion on prevention of iatrogenic urologic injury suggests a real need for improved education and training of gynecologic surgeons. Discontinued manufacturing of indigo carmine has led to the utilization of alternative methods to assess ureteral patency during cystoscopy, such as phenazopyridine or sodium fluorescein. Intraoperative cystoscopy has been shown to detect approximately 50% of urinary tract injuries during hysterectomy, but has limited accuracy and does not necessarily decrease delayed postoperative complications. When identified, most urologic injuries can be managed in a minimally invasive fashion. SUMMARY: A thorough understanding of pelvic anatomy and early recognition of urinary tract injuries can significantly reduce surgical morbidity for women undergoing laparoscopic surgery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".