Benefit of Allogeneic Transplantation in Patients Age ≥ 60 Years with Acute Myeloid Leukemia Is Limited to Those in First Complete Remission at Time of Transplant
Bibliographic record
Abstract
We evaluated the impact of age and remission status on 242 consecutive patients who underwent allogeneic hematopoietic cell transplantation for acute myeloid leukemia (AML) in our program between 1999 and 2011. Median age of all patients was 48 years (range, 18 to 71). Based on age and remission status, patients were divided into 4 groups: first complete remission (CR1) age <60 years (n = 116), second complete remission (CR2) age <60 years (n = 78), CR1 age ≥60 years (n = 32), and CR2 age ≥60 years (n = 16). Donors were matched related (n = 155, 64%) or matched unrelated (n = 87, 36%). Median follow-up of survivors was 65 months (range, 12 to 145). In a univariate analysis, 3-year overall survival rates of the 4 groups were 57%, 43%, 39%, and 16% (P = .003), respectively. In a multivariable analysis, hazard ratios of nonrelapse mortality and survival were 2.08 (P = .06) and 1.52 (P = .23), respectively, in patients ≥60 years in CR2 compared with ≥ 60 years in CR1. Although a plateau in survival was observed for patients ≥60 years in CR1 similar to those <60 years in CR1 and CR2, no long-term survivors were seen in patients ≥60 years in CR2. Our data suggest disappointing outcomes in AML patients ≥60 years of age transplanted in CR2. Therefore, if a transplant is indicated, early referral is recommended in patients ≥60 years with AML.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".