Incidence and socioeconomic factors in older adults with acute myeloid leukaemia: Real‐world outcomes from a population‐based cohort
Bibliographic record
Abstract
OBJECTIVES: Acute myeloid leukaemia (AML) is a disease of older adults, who are vulnerable to socio-economic factors. We determined AML incidence in older adults and the impact of socio-economic factors on outcomes. METHODS: We included 3024 AML patients (1996-2016) identified from a population-based registry. RESULTS: AML incidence in patients ≥60 years increased from 11.01 (2001-2005) to 12.76 (2011-2016) per 100 000 population. Among 879 patients ≥60 years in recent eras (2010-2016), rural residents (<100 000 population) were less likely to be assessed by a leukaemia specialist (39% rural, 47% urban, p = .032); no difference was seen for lower (43%, quintile 1-3) vs. higher (47%, quintile 4-5) incomes (p = .235). Similar numbers received induction chemotherapy between residence (16% rural, 18% urban, p = .578) and incomes (17% lower, 17% high, p = 1.0). Differences between incomes were seen for hypomethylating agent treatment (14% low, 20% high, p = .041); this was not seen for residence (13% rural, 18% urban, p = .092). Among non-adverse karyotype patients ≥70 years, 2-year overall survival was worse for rural (5% rural, 12% urban, p = .006) and lower income (6% low, 15% high, p = .017) patients. CONCLUSIONS: AML incidence in older adults is increasing, and outcomes are worse for older rural and low-income residents; these patients face treatment barriers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".