Use of radiotherapy for bladder cancer: A population-based study of evolving referral and practice patterns
Bibliographic record
Abstract
INTRODUCTION: Definitive treatment for muscle-invasive bladder cancer includes either cystectomy or radiotherapy (RT). We describe use of RT and radiation oncology (RO) referral patterns in the contemporary era. METHODS: The Ontario Cancer Registry and linked records of treatment were used to identify all patients who received cystectomy or RT for bladder cancer from 1994-2013. Physician billing records were linked to identify RO consultation before radical treatment. Multilevel logistic regression models were used to examine patient factors and physician-level variation in referral to RO and use of RT. RESULTS: A total of 7461 patients underwent cystectomy or RT for bladder cancer from 1994-2013; 5574 (75%) had cystectomy and 1887 (25%) had RT. Use of RT decreased from 43% (126/289) in 1994 to 23% (112/478) in 2008 and remained stable from 2009-2013 (23%, 507/2202). RO referral rate among all cases decreased from 46% (134/289) in 1994 to 30% (143/478) in 2008; however, the rates began to rise in the contemporary era from 31% (137/442) in 2009 to 37% (165/448) in 2013 (p=0.03). Patient factors associated with use of RT include older age, greater comorbidity, and geographic location. Surgeon-level factors associated with greater preoperative referral to RO include higher surgeon case volume and practicing in a teaching hospital. CONCLUSIONS: One-quarter of patients treated with curative intent therapy for bladder cancer receive RT. While referral rates to RO are increasing, future data will identify the extent to which this has altered practice. Collaborative efforts promoting multidisciplinary care and RO consultation before radical treatment are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".