Intra-Diverticular Bladder Tumours: How to Manage Rationally
Bibliographic record
Abstract
Objective To report changing practice in the management of intra-diverticular bladder tumours. Methods We undertook a review of all intra-diverticular bladder tumours in our prospectively maintained institutional database. Results A total of 28 patients (male = 27, female = 1) with a median age of 71 years (IQR 61 to 76) were diagnosed with intra-diverticular bladder tumours (IDBT) between March 2013 and February 2021. Fourteen had visible and 3 had non-visible haematuria, while 11 patients had lower urinary tract symptoms. Median axial diameter of the diverticula was 46 mm (IQR 35 to 69), and median neck diameter was 9 mm (IQR 7 to 11). All patients had CT-urography and 5 patients also had an MRI. Surgical treatment consisted of diverticulectomy (n = 11), diverticulectomy and ipsilateral ureteric reimplantation (n = 11), radical cystectomy and ileal conduit (n = 4), or radical cystectomy and orthotopic bladder (n = 2). Eleven patients had open procedures, and 17 had robotic assisted surgery. Final pathological stages were T0 (n = 2), Ta (n = 5), T1 (n = 7), T3a (n = 8) and T3b (n = 6). Twenty-four patients had urothelial carcinoma (including one nested variant and 4 with squamous differentiation) and 2 had small cell carcinoma. Three patients had neoadjuvant systemic chemotherapy, 2 had intravesical bacillus Calmette-Guerin (BCG) with mitomycin, and one had BCG monotherapy preoperatively. Five patients had adjuvant systemic chemotherapy while 7 had adjuvant intravesical therapies. Mean follow-up period was 37.8 months (±25.3). Mean recurrence-free survival was 61.5% (CI 45.7 to 77.4) and mean overall survival 71.6 % (CI 57.4 to 85.8). Ten patients (37%) died of cancer. Conclusion Management of intra-diverticular bladder tumours is evolving. Bladder-sparing approaches are gaining popularity. Robot-assisted diverticulectomy is preferable as it reduces the morbidity resulting from treatment.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".