A Risk Calculator for Post-Operative Urinary Retention (POUR) Following Vaginal Pelvic Floor Surgery: Multivariable Prediction Modelling
Bibliographic record
Abstract
Objective: To determine the peri-operative characteristics associated with an increased risk of post-operative urinary retention (POUR) following vaginal pelvic floor surgery. Design: A retrospective cohort study using multivariable prediction modelling. Setting: A tertiary referral urogynaecology unit. Population: Patients undergoing vaginal pelvic floor surgery from January 2015 to February 2020. Methods: Eighteen variables (24 parameters) were compared between those with and without POUR and then included as potential predictors in statistical models to predict POUR. The final model was chosen as the one with the largest c-index from internal cross-validation. This was then externally validated using a separate data set (n=94) from another surgical centre. Main Outcome Measures: diagnosis of POUR following surgery while the patient was in hospital. Results: Among the 700 women undergoing surgery, 301 (43%) experienced POUR. Pre-operative variables with statistically significant univariate relationships with POUR included age, menopausal status, prolapse stage, and uroflow parameters. Significant peri-operative factors included estimated blood loss, amount of intravenous fluid administered, operative time, length of stay, and specific procedures including vaginal hysterectomy with intraperitoneal vault suspension, anterior colporrhaphy, posterior colporrhaphy, and colpocleisis. The lasso logistic regression model had the best combination of internally cross-validated c-index (0.73) and accurate calibration curve. Using this data, a POUR risk calculator was developed (https://pourrisk.shinyapps.io/POUR/). Conclusions: This POUR risk calculator will allow physicians to counsel patients pre-operatively on their risk of developing POUR after vaginal pelvic surgery and help focus discussion around potential management options.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".