The use of perioperative chemotherapy in patients undergoing radical cystectomy for bladder cancer in Quebec (Canada), 2000-2016
Bibliographic record
Abstract
INTRODUCTION: Despite its proven benefit, studies have reported poor use of perioperative chemotherapy (POC) in bladder cancer patients undergoing radical cystectomy (RC). We evaluated POC use in Quebec between January 2000 and September 2016. METHODS: Using provincial health administrative databases, data were retrospectively collected from patients from two years before RC until December 2016 or death. Logistic regression was used to identify variables predicting POC use. Survival analyses were conducted using Cox regression. Analyzed covariates were age, sex, comorbidities, year of RC, residence and hospital region, distance to hospital, hospital type and size, and hospital's and surgeon's RC volume. RESULTS: A total of 790/4656 patients (17.0%) received POC. Neoadjuvant chemotherapy (NAC) use increased in recent years: 3.5% (2009), 11.2% (2012), and 20.7% (2015). POC use was increased in patients with recent surgery, a younger age, less comorbidities, residing closer to the hospital of surgery, and a high surgeon's RC volume (p<0.05). For patients treated between 2013 and 2016, a younger age (odds ratio [OR] 0.71; 95% confidence interval [CI] 0.64-0.80 per five years), shorter distance to the hospital (OR 0.88; 95% CI 0.77-0.99 per 50 km), surgery in an academic hospital (OR 1.86; 95% CI 1.06-3.29), and recent surgery (OR 1.34; 95% CI 1.14-1.58 per year) independently predicted NAC use. These NAC users had a significantly higher overall survival rate than patients without POC (hazard ratio 0.73; 95% CI 0.55-0.97). Limitations include missing data on pathological staging. CONCLUSIONS: NAC/POC use increased in Quebec but was lower compared to most developed countries. Its use was lower in patients residing further from the hospital and in those treated in non-academic hospitals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".