CAR-T Access Disparities for Multiple Myeloma in the Midwest: A Social Determinants of Health Perspective
Bibliographic record
Abstract
BACKGROUND: Multiple Myeloma (MM) is the most common type of blood cancer among black individuals. CAR-T therapy is crucial, but often inaccessible to many black patients and those from underserved communities. The University of Kansas Health System administers over 100 CAR-T treatments annually and aims to evaluate barriers to CAR-T therapy access related to the social determinants of health in the Midwest area. METHODS: This study examined patients with MM referred for CAR-T therapy from January 2021 to December 2023, assessing how race, socioeconomic status, and insurance influenced eligibility for leukapheresis. Data on income and travel were gathered from the 2022 US Census and analyzed using R software. RESULTS: The study included 271 referrals for MM CAR-T therapy involving 179 patients, with a median age of 66 years (51% male). DEMOGRAPHICS: 80% white, 16% black, 2.2% other races, 1.8% Asian, with a median income of $70,644. Nearly half lived more than 30 min from the center (Mainly from Kansas, Missouri and Nebraska). Apheresis rates were similar across racial groups: 54% for whites, 54% for blacks, and 50% for others, while none of the three Asian patients proceeded. Nine patients (5%) could not proceed because of caregiver or insurance barriers, and cell collection rates were comparable regardless of distance (34% vs. 35%). CONCLUSION: This study showed that black representation in CAR-T access matches local demographics, indicating less disparity among minorities. Unlike national reports, distance, income, and insurance do not significantly affect access, suggesting the need for a national study on the social determinants impacting CAR-T access for multiple myeloma.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".