Predictive value of karyotype on outcome of autotransplants for acute myeloid leukemia in second remission
Bibliographic record
Abstract
The impact of karyotype on the outcome of patients who undergo autotransplant for acute myeloid leukemia (AML) in second remission (CR2) has not been explored. We evaluated the outcomes of 40 patients who proceeded to autotransplant for AML in CR2 at 2 centers. The median age at autotransplant was 50 years (18-64 years) and the median duration of first remission was 15 months (0.8-51 months). High-dose therapy was melphalan 140-160 mg/m2 plus etoposide 60 mg/kg with or without total body irradiation (22), a busulfan-based regimen (17), and cyclophosphamide alone (1). Six patients (15%) died of treatment-related causes within the first 100 days. Event-free and overall survival at 3 years were both 38% (95% confidence interval 23-53%). At a median follow-up of 76 months (2?-?170) in surviving patients, 13 (32.5%) are alive and disease free. Graft purging did not significantly influence survival outcome (P=0.94), although platelet engraftment was significantly delayed (P=0.02). The relative risk of an event (relapse or death) for the karyotype risk groups was favorable 1.0; intermediate 4.2 (1.2-14.7); adverse 9.9 (1.5-63.9); unknown 2.3 (0.6-8.8) (P=0.028). We conclude that patients with AML in CR2 who undergo autotransplant can have durable remissions and those with a good risk karyotype are the most likely to obtain long-term disease-free survival.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".