Inferior Access to Allogeneic Transplant in Disadvantaged Populations: A Center for International Blood and Marrow Transplant Research Analysis
Bibliographic record
Abstract
Allogeneic hematopoietic cell transplantation (alloHCT) is offered in a limited number of medical centers and is associated with significant direct and indirect costs. The degree to which social and geographic barriers reduce access to alloHCT is unknown. Data from the Surveillance, Epidemiology and End Results Program (SEER) and the Center for International Blood and Marrow Transplant Research (CIBMTR) were integrated to determine the rate of unrelated donor (URD) alloHCT for acute myelogenous leukemia (AML), acute lymphoblastic leukemia (ALL), and myelodysplastic syndrome (MDS) performed between 2000 and 2010 in the 612 counties covered by SEER. The total incidence of AML, ALL, and MDS was determined using SEER, and the number of alloHCTs performed in the same time period and geographic area were determined using the CIBMTR database. We then determined which sociodemographic attributes influenced the rate of alloHCT (rural/urban status, median family size, percentage of residents below the poverty line, and percentage of minority race). In the entire cohort, higher levels of poverty were associated with lower rates of alloHCT (estimated rate ratio [ERR], .86 for a 10% increase in the percentage of the population below the poverty line; P < .01), whereas rural location was not (ERR, .87; P = .11). Thus, patients from areas with higher poverty rates diagnosed with ALL, AML, and MDS are less likely patients from wealthier counties to undergo URD alloHCT. There is need to better understand the reasons for this disparity and to encourage policy and advocacy efforts to improve access to medical care for all.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".