Prevention and management of urologic injury during gynecologic laparoscopy
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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