The neoadjuvant management of muscle-invasive bladder cancer (MIBC) in Canada: A national survey of urologists.
Bibliographic record
Abstract
303 Background: There is level 1 evidence and a 5% absolute survival benefit supporting the use of cisplatin-based neoadjuvant chemotherapy (NC) for the management of MIBC. Despite this, it is well known that the majority of eligible patients undergoing cystectomy do not receive NC. We previously surveyed medical oncologists and found that the majority will offer NC to MIBC patients depending on stage, renal function, performance status (PS), and comorbidities. However, the number of MIBC patients being referred for consideration of NC by urologists remains low. The aim of this followup survey to urologists was to better understand their approach to MIBC, and referral patterns for NC. Methods: A survey consisting of 24 questions was administered to Canadian urologists belonging to the Canadian Urologic Oncology Group. Respondents completed the survey and mailed/faxed back their responses. The survey was similar to, but not identical to the previous medical oncology survey. Results: Of the 25 respondents, 21/25 (84%) were academic, >90% were in full-time practice, and 72% were practising for >10 yrs. Most (84%) treated over 20 bladder cancer cases annually. Overall, 22/25 (80%) will offer a NC approach if appropriate. In 2009, 9/24 (38%) sent >6 referrals for NC; 2/24 (25%) sent 5-6 referrals, 6/24 (20%) sent 3-4 referrals, and 5/24 (8%) sent 1-2 referrals. NC was offered as standard of care or to downsize tumors. Initial staging included cystoscopy, CT chest/abdo/pelvis and bone scan. Key factors cited for not offering NC were: T2a disease, GFR <40ml/min, age >85 or PS 3 or 4. Average time from NC to cystectomy was 4-6 wks. Conclusions: The majority of academic urologists in Canada will refer MIBC patients for NC except those with T2a disease, poor renal function, age >85 or poor PS. Non-academic urologists are underrepresented in this survey, and may represent the group facing the greatest challenges in offering NC, due to issues such as access to medical oncology, or lack of local expertise in managing MIBC. Targeting non-academic urologists, and encouraging consultation with a medical oncologist for all patients with MIBC, may lead to increased utilization of NC, and better outcomes in this disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".