Income Disparities in Survival and Receipt of Neoadjuvant Chemotherapy and Pelvic Lymph Node Dissection for Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
Background: Muscle-invasive bladder cancer (MIBC) is a potentially fatal disease, especially in the setting of locally advanced or node-positive disease. Adverse outcomes have also primarily been associated with low-income status, as has been reported in other cancers. While the adoption of neoadjuvant cisplatin-based chemotherapy (NAC) followed by radical cystectomy (RC) and pelvic lymph node dissection (PLND) has improved outcomes, these standard-of-care treatments may be underutilized in lower-income patients. We sought to investigate the economic disparities in NAC and PLND receipt and survival outcomes in MIBC. Methods: Utilizing the National Cancer Database, a retrospective cohort analysis of cT2-4N0-3M0 BCa patients with urothelial histology who underwent RC was conducted. The impact of income level on overall survival (OS) and the likelihood of receiving NAC and PLND was evaluated. Results: A total of 25,823 patients were included. This study found that lower-income patients were less likely to receive NAC and adequate PLND (≥15 LNs). Moreover, lower-income patients exhibited worse OS (Median OS 55.9 months vs. 68.2 months, p < 0.001). Our findings also demonstrated that higher income, treatment at academic facilities, and recent years of diagnosis were associated with an increased likelihood of receiving standard-of-care modalities and improved survival. Conclusions: Even after controlling for clinicodemographic variables, income independently influenced the receipt of standard MIBC treatments and survival. Our findings identify an opportunity to improve the quality of care for lower-income MIBC patients through concerted efforts to regionalize multi-modal urologic oncology care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".