Disease biology rather than age is the most important determinant of survival of patients ≥ 60 years with acute myeloid leukemia treated with uniform intensive therapy
Bibliographic record
Abstract
BACKGROUND: The objectives of the current study were to evaluate the outcome of patients > or = 60 years with acute myeloid leukemia (AML) treated uniformly with high-dose daunorubicin containing induction and modified high-dose cytosine arabinoside containing postremission therapy, and to identify factors predictive of complete disease remission (CR) and survival. METHODS: Between 1998 and 2002, the authors treated 117 newly diagnosed patients (acute promyelocytic leukemia excluded) with AML > or = 60 years (median, 67 years; range, 60-82 years). Karyotype (Medical Research Council classification) at diagnosis was categorized as good risk (n = 3), intermediate risk (n = 69), adverse risk (n = 26), and suboptimal/not done (n = 19). A normal karyotype was seen in 41 patients and 40 (34%) had secondary AML. RESULTS: The outcome of induction included the following: CR, 62 (53%); early death, 5 (4%); death during hypoplasia, 14 (12%); and resistant disease, 36 (31%). The 3-year event-free (EFS) and overall survival (OS) rates were 9% (95% confidence interval [95% CI], 3-16%) and 17% (95% CI, 9-29%), respectively. In a univariate analysis, cytogenetics, lactate dehydrogenase level, leukocyte count, and performance status were the significant factors for EFS and OS. Age was not a significant prognostic factor for either CR or survival. In a multivariate model, adverse-risk cytogenetics, previous history of myelodysplastic syndrome or antecedent hematologic disorder, and high leukocyte count (> 30 x 10(9)/L) were independent adverse prognostic factors for survival. The impact of adverse karyotype on EFS and OS was time dependent and was observed after 50 and 150 days, respectively. CONCLUSIONS: The authors concluded that candidacy for intensive therapy in older patients should be based on biologic features of disease and fitness, rather than on age.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".