Predictors of referral for neoadjuvant chemotherapy prior to radical cystectomy for muscle-invasive bladder cancer and changes in practice over time
Bibliographic record
Abstract
INTRODUCTION: In patients with non-metastatic muscle-invasive bladder cancer (MIBC) fit for curative therapy, a multidisciplinary approach consisting is recommended. This approach includes local treatment (usually radical cystectomy), ideally combined with neoadjuvant chemotherapy (NACT). Despite a survival benefit with NACT, uptake remains low. We assessed NACT consultation in Alberta and examined associative factors, as well as the relationship to survival. METHODS: Patients with MIBC were identified through the Alberta Cancer Registry. Demographic and clinicopathologic information was collected from electronic medical records between 2007 and 2011. In addition to descriptive statistics, logistic regression was used to determine factors associated with receiving NACT consultation. Overall survival was described using a Kaplan-Meier estimate. RESULTS: Of the 315 radical cystectomy patients, 140 (45.1%, 95% confidence interval [CI] 39.5, 50.8) received NACT consultation. Patients ≥80 years (odds ratio [OR] 0.21, 95% CI 0.08, 0.57, p = 0.002) and those treated in Calgary (OR 0.11, 95% CI 0.05, 0.25, p < 0.001) were less likely to receive NACT consultation. The rate of NACT consultation increased steadily from 2007 to 2011 (OR 1.23, 95% CI 1.04, 1.45 per year of diagnosis, p = 0.018). After a median follow-up of 28.1 months (range: 14.6-50.3), median survival was 54.7 months for patients who received NACT consultation versus 31.2 months for those who did not (p = 0.030). CONCLUSIONS: NACT consultation in patients with MIBC undergoing radical cystectomy has improved over time; however, regional differences underscore the need for a standardized approach to NACT consultation, including common referral mechanisms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".