Inequalities in CAR T-cell therapy access for US patients with relapsed/refractory DLBCL: a SEER-Medicare data analysis
Bibliographic record
Abstract
ABSTRACT: Chimeric antigen receptor (CAR) T-cell (CAR-T) therapy has shown curative potential for patients with diffuse large B-cell lymphoma (DLBCL) and other malignancies, but its accessibility among Medicare patients, particularly in disadvantaged populations, remains uncertain. This study aims to assess CAR-T use among Medicare patients with DLBCL receiving third-line or later (3L+) treatment, focusing on access disparities and their impact on clinical outcomes. Using Surveillance, Epidemiology, and End Results (SEER)-Medicare data from 2007 to 2020, multivariate logistic regression was used to evaluate patient characteristics and the effects of distance to authorized treatment centers (ATCs) on CAR-T access. Between 2017 and 2020, 2241 patients were treated for 3L+ DLBCL in the SEER-Medicare data, of whom 122 (5.4%) received CAR-Ts. CAR-T recipients were less likely to have multiple comorbidities (odds ratio [OR], 0.904; P = .001) but more likely to live in higher income areas (OR, 1.176; P = .004). If distance to the nearest ATC for "poor-access" states (average distance to ATC, 104.4 miles) decreased to the average distance in "better-access" states (34.2 miles), there would be a 37.6% increase in number of patients receiving CAR-Ts (6.6%-9.1%; P < .001). These findings highlight substantial disparities in CAR-T use, driven by geographic and socioeconomic factors. Addressing these barriers could significantly enhance equitable access to CAR-T therapy and improve outcomes for underserved populations, emphasizing the need for targeted interventions to reduce geographic and systemic barriers to care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".