Urinary Tract Injury at Benign Gynecologic Surgery and the Role of Cystoscopy
Bibliographic record
Abstract
OBJECTIVE: To calculate the rates of urinary tract injury detected during and after benign gynecologic surgery. To explore the role of routine intraoperative cystoscopy and determine if it helps in reducing injuries detected postoperatively. DATA SOURCES: We conducted a literature search for urinary tract injuries at benign gynecologic surgery in PubMed, EMBASE, ClinicalTrials.gov, and Web of Science from January 2004 to August 2014. We combined our results with a database from a previously published systematic review to include earlier studies. METHODS OF STUDY SELECTION: A total of 79 studies met our inclusion criteria. Excluded were letters to the editor, studies involving only selective cystoscopy in higher risk patients, case reports, and reports that included injuries resulting from obstetric or oncologic procedures. TABULATION, INTEGRATION, AND RESULTS: Data from each report were classified according to type of surgery into vaginal hysterectomy, abdominal hysterectomy, laparoscopic hysterectomy, other (nonrobotic) gynecologic and urogynecologic surgery, robotic hysterectomy, and other robotic gynecologic and urogynecologic surgery. We determined the ureteric and bladder injury rates for each surgery type from studies in which routine intraoperative cystoscopy was performed and separately from studies in which it was not performed. Intraoperatively detected rates of ureteric and bladder injury were markedly higher with routine intraoperative cystoscopy. We obtained an adjusted ureteric injury rate of 0.3% and a bladder injury rate of 0.8%. The estimated postoperative ureteric injury detection rates per 1,000 surgeries were 1.6 without routine cystoscopy and 0.7 with routine cystoscopy. Postoperative bladder injury detection rates per 1,000 surgeries were 0.8 without routine cystoscopy and 1.0 with routine cystoscopy. CONCLUSION: Although routine cystoscopy clearly increases the intraoperative detection rate of urinary tract injuries, this systematic review of 79 mostly retrospective studies shows that it does not appear to have much effect on the postoperative injury detection rate.
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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 | 0.016 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".