Utilization of allogeneic hematopoietic stem cell transplantation among patients with newly diagnosed acute myeloid leukemia in California: a population-based linked dataset study
Bibliographic record
Abstract
Acute myeloid leukemia (AML) often requires allogeneic hematopoietic cell transplantation (alloHCT) for cure, but historically alloHCT has been strikingly underutilized. Reasons for this remain uncertain at the population level. We examined alloHCT utilization over time and explored associations between demographic / healthcare factors and use of alloHCT by age group (adolescent / young adult [AYA] 15-39 years, adult 40-64 years, older adult 65-79 years) using a linked dataset merging the Center for Internatonal Blood and Marrow Transplant Research, the California Cancer Registry, and the California Patient Discharge Database. Eligibility included patients newly diagnosed with AML in California between 2001-2016 who received induction therapy and had no prior HCT. Multivariable Fine-Gray regression analyses were fitted separately across age groups. Among 7,925 patients with AML, alloHCT utilization increased over time across all age groups; however, in the most recent time period studied (2011-2016), utilization within two years of diagnosis remained lowest in older adults (13%) relative to adults (41%) and AYA (49%). Factors statistically significantly associated with lower alloHCT utilization were: 1) AYA: female sex, lower neighborhood socioeconomic status (nSES), uninsured or Indian Health Services (IHS) coverage; 2) adults: older age, male sex, non-Hispanic Black or Asian race and ethnicity, unmarried, lower nSES, uninsured or covered by Medicaid, Medicare, or IHS, higher comorbidity, and living 100+ miles from a transplant center; and 3) older adults: older age, Asian race, and unmarried. In conclusion, using a population-based linked dataset, we demonstrate that utilization of alloHCT among older patients newly diagnosed with AML remains low in California, and factors associated with utilization vary by age group.
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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 | 0.000 | 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".