Association of race/ethnicity and insurance with survival in patients with diffuse large B‐cell lymphoma in a large real‐world cohort
Bibliographic record
Abstract
Abstract Objective Few studies have evaluated disparities in race, ethnicity, and health insurance in real‐world health outcomes for patients with diffuse large B‐cell lymphoma (DLBCL). This study aimed to evaluate association between racial disparities and health insurance with real‐world health outcomes. Methods Patients with DLBCL (January 2011–July 2021) treated with first‐line therapy were selected from a real‐world database. Variables of interest included race/ethnicity, health insurance type (Medicaid, Commercial) by patient age (<65, ≥65 years), stage at diagnosis, overall survival (OS), and time to second‐line therapy or death due to any cause (TTNTD). Results Among 5362 patients with DLBCL (82% White, 7% Black, 8% Hispanic/Latino, 3% Asian), White patients were older (mean age, 66.7 vs. 59.3–62.5 years) and less likely to have Medicaid insurance (1.7% vs. 3.4%–5.9%). Adjusted hazard ratios (aHR) for OS (Black, 0.88 [95% confidence interval, 0.72–1.07]; Hispanic/Latino, 0.84 [0.70–1.03]; Asian, 0.82 [0.59–1.16]) and TTNTD (Black, 0.89 [0.75–1.05]; Hispanic/Latino, 0.85 [0.73–1.00]; Asian, 1.11 [0.86–1.43]) were similar to those of White patients. Among patients aged <65 years, Medicaid‐insured versus Commercially insured patients had more advanced disease (stage III–IV, 66% vs. 48%), worse OS (aHR, 0.52 [0.34–0.80]; p = 0.003), and shorter TTNTD (aHR, 0.70 [0.49–0.99]; p = 0.044). Conclusions There was no statistically significant difference in these variables/outcomes between Medicaid‐insured and commercially insured patients aged ≥65 years. Medicaid‐insured status was significantly associated with poorer OS and TTNTD in patients with DLBCL aged <65 years but not in those aged ≥65 years, with or without adjusting for other baseline characteristics. Race was not significantly associated with these outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".