A risk calculator for <scp>postoperative</scp> urinary retention (<scp>POUR</scp>) following vaginal pelvic floor surgery: multivariable prediction modelling
Bibliographic record
Abstract
OBJECTIVE: To determine the perioperative characteristics associated with an increased risk of postoperative 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 model with the largest concordance index (c-index) from internal cross-validation. This was then externally validated using a separate data set (n = 94) from another surgical centre. MAIN OUTCOME MEASURE: Diagnosis of POUR following surgery while the patient was in hospital. RESULTS: Among the 700 women undergoing surgery, 301 (43%) experienced POUR. Preoperative variables with statistically significant univariate relationships with POUR included age, menopausal status, prolapse stage and uroflowmetry parameters. Significant perioperative factors included estimated blood loss, volume 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, 95% CI 0.71-0.74) and a calibration curve that showed good alignment between observed and predicted risks. 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 preoperatively 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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".