Risk factors for urinary retention after urogynecologic surgery: A retrospective cohort study and prediction model
Bibliographic record
Abstract
AIMS: Postoperative urinary retention (POUR) is a common complication of urogynecological surgery. Our study aimed to identify demographic and perioperative risk factors to construct a prediction model for POUR in urogynecology. METHODS: Our retrospective cohort study reviewed all patients undergoing pelvic reconstructive surgeries at our tertiary care center (Jan 1, 2013-May 1, 2019). Demographic, pre-, intra- and postoperative variables were collected from medical records. The primary outcome, POUR, was defined as (1) early POUR (E-POUR), failing initial trial of void or; (2) late POUR (L-POUR), requiring an indwelling catheter or intermittent catheterization on discharge. Risk factors were identified through univariate and multivariate logistic regression analyses. A clinical prediction model was constructed with the most significant and clinically relevant risk factors. RESULTS: In 501 women, 182 (36.3%) had E-POUR and 61 of these women (12.2% of the entire cohort) had L-POUR. Multivariate logistic regression revealed preoperative postvoid residual (PVR) over 200 ml (odds ratio [OR]: 3.17; p = 0.026), voiding dysfunction symptoms extracted from validated questionnaires (OR: 3.00; p = 0.030), and number of concomitant procedures (OR: 1.30 per procedure; p = 0.021) as significant predictors of E-POUR; preoperative PVR more than 200 ml (OR: 4.07; p = 0.011) and antiincontinence procedure with (OR: 3.34; p = 0.023) and without (OR: 2.64; p = 0.019) concomitant prolapse repair as significant predictors of L-POUR. A prediction model (area under the curve: 0.70) was developed for E-POUR. CONCLUSIONS: Elevated preoperative PVR is the most significant risk factor for POUR. Alongside other risk factors, our prediction model for POUR can be used for patient counseling and surgical planning in urogynecologic surgery.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".