Trends and disparities in the use of neoadjuvant chemotherapy for muscle-invasive urothelial carcinoma
Bibliographic record
Abstract
INTRODUCTION: Neoadjuvant chemotherapy (NAC) prior to radical or partial cystectomy is considered the standard of care for eligible patients with muscle-invasive urothelial carcinoma. Despite guideline recommendations, adoption of NAC has historically been low, although prior studies have suggested that use is increasing. In this contemporary study, we examine trends in the use of NAC and explore factors associated with its receipt. METHODS: We identified patients in the National Cancer Database who underwent radical or partial cystectomy for cT2-cT4N0M0 urothelial carcinoma from 2006-2014. The proportion of patients receiving NAC during each year was examined. Logistic regression models were used to evaluate clinical and socioeconomic factors associated with the receipt of NAC. RESULTS: A total of 18 188 patients were identified who underwent radical or partial cystectomy for muscle-invasive bladder cancer. Overall, 3940 (21.7%) received NAC. We noted a significant increase in the use of NAC over time, from 9.7% in 2006 to 32.2% in 2014. Factors associated with lower use of NAC include older age, higher comorbidity score, lower cT stage, lower hospital radical cystectomy volume, treatment at a non-academic facility, lower patient income, and receipt of partial cystectomy (all p<0.001). Interestingly, neither sex nor race were associated with receipt of NAC. CONCLUSIONS: Use of NAC has increased significantly over time to a modest rate of 32%. However, disparities still exist in the receipt of NAC, and future efforts aimed at mitigating these disparities 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".