A novel tool to assess the risk of urinary incontinence after nerve‐sparing radical prostatectomy
Bibliographic record
Abstract
UNLABELLED: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: Urinary incontinence is one of the most important morbidities after radical prostatectomy that has detrimental effect on the postoperative quality of life. The present study provides an accurate and dynamic multivariable risk stratification tool that predicts the postoperative urinary incontinence risk after radical prostatectomy based on patient-related as well as surgeon-related variables. OBJECTIVE: To develop a multivariable risk classification tool to estimate postoperative urinary incontinence (UI) risk as UI represents one of the most disabling surgical sequelae after radical prostatectomy (RP). PATIENTS AND METHODS: We evaluated 1311 patients treated with nerve-sparing RP between 2006 and 2010 at our institution. Regression tree analysis was used to stratify patients according to their postoperative UI risk. Kaplan-Meier curve estimates were used to assess the UI rate in the novel UI-risk groups. The discrimination of the novel tool was measured with the area under the curve method. RESULTS: At 3, 6 and 12 months, the UI rates were 44%, 26% and 12%, respectively. Regression tree analysis stratified patients into high risk (International Index of Erectile Function - Erectile Function domain [IIEF-EF] = 1-10), intermediate risk (IIEF-EF > 10 and age ≥ 65 years), low risk (IIEF-EF > 10, age < 65 years and body mass index [BMI] ≥ 25 kg/m(2) ) and very low risk (IIEF-EF > 10, age < 65 years and BMI < 25 kg/m(2) ) groups. The 3-month UI rates in these groups were 37%, 43%, 45% and 48%, respectively. The 6-month UI rates were 19%, 23%, 29% and 34%, respectively. The 12-month UI rates were 7%, 13%, 14% and 15%, respectively (log-rank P < 0.001). The area under the curve was 71%, 70% and 68% at 3, 6 and 12 months, respectively. CONCLUSIONS: We developed the first risk classification tool that predicts patients at high risk of UI after RP. These consisted mainly of individuals who were impotent before RP, elderly and/or overweight. This tool can be used for patient counselling.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".