Diabetes and elevated urea level predict for uretero-ileal stricture after radical cystectomy and ileal conduit formation
Bibliographic record
Abstract
INTRODUCTION: Benign uretero-ileal anastomotic stricture is a significant complication following radical cystectomy and ileal conduit urinary diversion after radical cystectomy. We examined risk factors for stricture formation to predict those at greatest stricture risk. METHODS: A retrospective chart review was conducted for patients undergoing radical cystectomy and ileal conduit diversion between 2002 and 2012. Demographic data and patient variables were analyzed to determine risk factors for uretero-ileal stricture using multivariate logistic regression. RESULTS: Over the study period, 133 patients underwent cystectomy and ileal conduit formation, with 14 (10.5%) developing uretero-ileal anastomotic stricture. Diabetes and elevated serum urea level (defined as >7.1 mmol/L) were associated with increased risk for development of uretero-ileal stricture (odds ratio 4.31 and 4.28, respectively; p<0.05 for each). CONCLUSIONS: In this patient cohort, diabetes and elevated serum urea level were predictive for the development of uretero-ileal anastomotic stricture. Further prospective study with larger patient samples is required.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".