Rates of Urinary Tract Injury From Gynecologic Surgery and the Role of Intraoperative Cystoscopy
Bibliographic record
Abstract
OBJECTIVE: To estimate the rates of urinary tract injury after benign gynecologic surgery. To explore the role of routine intraoperative cystoscopy at benign gynecologic surgery. DATA SOURCES: We conducted a systematic MEDLINE search for urinary tract injuries at gynecologic surgery for the period from November 1998 to May 2004 and combined this with a previous systematic review performed in the same fashion for the period from January 1966 to October 1998. METHODS OF STUDY SELECTION: There were 47 studies that fit our inclusion criteria: 29 that did not use routine intraoperative cystoscopy, 17 that used routine intraoperative cystoscopy, and 1 that reported the frequency of urinary tract injury separately, with and without routine intraoperative cystoscopy. We determined the crude and fitted ureteric and bladder injury rates for each surgery type from the studies where routine intraoperative cystoscopy was not performed and then from the studies where routine intraoperative cystoscopy was performed. TABULATION, INTEGRATION, AND RESULTS: From studies without routine cystoscopy, combined ureter and bladder injury rates varied according to the complexity of the surgery, ranging from less than 1 injury per 1000 for subtotal hysterectomy with or without bilateral salpingo-oophorectomy to as many as 13 injuries per 1000 surgeries for laparoscopic hysterectomy with or without bilateral salpingo-oophorectomy and for other gynecologic and urogynecologic surgeries. Injury rates were higher when routine intraoperative cystoscopy was used, but the confidence intervals were wider. CONCLUSION: The reasons for higher injury detection rates when routine cystoscopy was performed are unclear. Further study is needed to identify the scenarios where routine cystoscopy is warranted after major gynecologic 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 | 0.020 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".