Perioperative chemotherapy for muscle‐invasive bladder cancer: A population‐based outcomes study
Bibliographic record
Abstract
BACKGROUND: Practice guidelines recommend neoadjuvant chemotherapy (NACT) for bladder cancer. However, the evidence in support of adjuvant chemotherapy (ACT) is less robust. Here we describe whether the evidence of efficacy for NACT/ACT was sufficient to change clinical practice and whether the efficacy demonstrated in clinical trials was translated into effectiveness in the general population. METHODS: Electronic records of treatment were linked to the population-based Ontario Cancer Registry to identify all patients with bladder cancer treated with cystectomy in Ontario 1994-2008. Utilization of NACT/ACT was compared across 1994-1998, 1999-2003, and 2004-2008. Logistic regression was used to analyze factors associated with NACT/ACT. Cox model and propensity score analyses were used to explore the association between ACT and survival. RESULTS: Two thousand forty-four patients underwent cystectomy for muscle-invasive bladder cancer (MIBC). Use of NACT remained stable (mean, 4%), whereas utilization of ACT increased over time (16%, 18%, 22%; P = .001). Advanced stage (T3/T4; OR, 1.83; 95% CI, 1.38-2.46) and node-positive disease (OR, 8.10; 95% CI, 6.20-10.7) were associated with greater utilization of ACT. Five-year overall survival (OS) and cancer-specific survival (CSS) for all patients was 29% (95% CI, 28%-31%) and 33% (95% CI, 31%-35%), respectively. Utilization of ACT was associated with improved OS (HR, 0.71; 95% CI, 0.62-0.81) and CSS (HR, 0.73; 95% CI, 0.64-0.84). Results were consistent in propensity score analyses. CONCLUSIONS: NACT remains substantially underutilized in routine clinical practice. Our results suggest that perioperative chemotherapy is associated with a substantial survival benefit in the general population. Patients who are planning to undergo cystectomy for bladder cancer should be reviewed by a multidisciplinary team.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".