Evaluation of the Morbidity of Routine Cystoscopy Performed Intraoperatively During Total Laparoscopic Hysterectomies
Bibliographic record
Abstract
STUDY OBJECTIVES: The primary objective is to determine the rate of morbid events (urinary tract infection, hematuria, urinary retention, false positive, incidental finding) associated with routine cystoscopies performed intraoperatively during total laparoscopic hysterectomies (TLH). The secondary objectives are 1) to determine the rate of urinary complications during TLHs in our centers and 2) to determine the detection rate of urinary complications using cystoscopy during TLHs. METHOD: Descriptive retrospective multicenter study. The study took place in Obstetrics & Gynecology departments of 2 university centers in Montreal. Patients underwent a routine cystoscopy during their TLH for a benign reason in our centers. Five hundred thirty-one charts from January 1, 2012 to January 31, 2018 were reviewed. RESULTS: The morbidity rate of routine cystoscopies during TLHs is 4.19% (22/524 cases) in our centers. Our urinary complication rate is 2.45% (13/531 cases). Of these 13 complications, 4 were detected by cystoscopy. CONCLUSION: The usefulness of routine cystoscopies performed intraoperatively during TLHs is questionable due to the number of morbid events and the low rate of urinary trauma in our centers. However, it is hard to establish a direct causality link between certain morbid events and cystoscopy. More studies should be conducted on this subject.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".