Treatment of muscle-invasive bladder cancer in Canada: A survey of genitourinary medical oncologists and urologists
Bibliographic record
Abstract
INTRODUCTION: Uptake of neoadjuvant chemotherapy (NC) for muscle invasive bladder cancer (MIBC) has been low despite evidence of a survival benefit. The primary aim of this study was to better understand why the rates are low and determine what factors specifically influence the decision to recommend NC for MIBC. METHODS: A 31-question survey was emailed between 2009 and 2011 to medical oncologists belonging to the Canadian Association of Genitourinary Medical Oncologists (CAGMO); and to urologists belonging to the Canadian Urologic Oncology Group (CUOG). We gathered data on practice characteristics, referrals for NC, factors influencing NC use, and chemotherapy regimens offered. Responses were summarized using descriptive statistics. RESULTS: In total, 26/30 (87%) medical oncologists and 25/84 (30%) urologists, who were primarily academic, completed the survey. Most clinicians (medical oncologists 96%, urologists 88%) recommended NC for MIBC, because they considered it to be the standard of care, but most medical oncologists saw ≤6 referrals annually. Performance status, presence of comorbidities and renal function were key considerations in offering NC. NC was not offered if performance status ≥2 (medical oncologists 38%, urologists 44%), age >80 (medical oncologists 46%, urologists 39%), or glomerular filtration rate ≤40 mL/min (medical oncologists 81%, urologists 50%). CONCLUSIONS: Most academic clinicians in Canada believe that cisplatin-based combination NC is the standard of care for MIBC and recommend it for patients with adequate performance status and renal function. Using a multidisciplinary approach to treat this disease may be one strategy to increase referral rates for NC and uptake of NC.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".