Is there a gender effect in bladder cancer? A population-based study of practice and outcomes
Bibliographic record
Abstract
INTRODUCTION: The incidence of bladder cancer varies by gender. Whether differences exist between women and men in extent of disease, treatment, and outcome is not well-described. We evaluate gender differences in bladder cancer using a population-based cohort. METHODS: Electronic records of treatment were linked to the population-based Ontario Cancer Registry to identify all patients with bladder cancer treated with cystectomy or radical radiotherapy (RT) in Ontario between 1994 and 2008. We compare extent of disease at time of cystectomy, treatment, and outcomes between women and men. RESULTS: In total, 5259 patients with bladder cancer were treated with cystectomy or radical RT; of these, 25% (n = 1296) were women. There was no gender difference in the proportion of patients treated with cystectomy (75% of women [974/1296], 73% of men [2905/3963], p = 0.189). At the time of cystectomy, women were more likely to have muscle-invasive disease (86% [836/974] vs. 80% [2335/2905], p < 0.001), but less likely to have lymph nodes dissected (68% [664/974] vs. 76% [2210/2905], p < 0.001]. Among the 2944 patients with muscle-invasive urothelial carcinoma treated with cystectomy, use of neoadjuvant (5% vs. 4%, p = 0.419) and adjuvant chemotherapy (18% vs. 20%, p = 0.190) did not differ significantly between genders. Five-year cancer-specific survival and overall survival of the full cohort did not differ between women and men (38% vs. 39%, p = 0.522; 33% vs. 33%, p = 0.795). CONCLUSIONS: This population-based cohort did not demonstrate any substantial differences in extent of disease, treatment, or outcome between women and men treated with cystectomy or radical RT for bladder cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".