Characteristics Predicting Outcomes of Allogeneic Stem-Cell Transplantation in Relapsed Acute Myelogenous Leukemia
Bibliographic record
Abstract
BACKGROUND: Allogeneic hematopoietic stem-cell transplantation (ahsct) is associated with significant morbidity and mortality, but it can cure carefully selected patients with acute myeloid leukemia (aml) in second remission (cr2). In a cohort of patients with aml who underwent ahsct in cr2, we determined the pre-transplant factors that predicted for overall survival (os), relapse, and non-relapse mortality. We also sought to validate the prognostic risk groups derived by Michelis and colleagues in this independent population. METHODS: In a retrospective chart review, we obtained data for 55 consecutive patients who underwent ahsct for aml in cr2. Hazard ratios were used to describe the independent effects of pre-transplant variables on outcome, and Kaplan-Meier curves were used to assess outcomes in the three prognostic groups identified by Michelis and colleagues. RESULTS: At 1, 3, and 5 years post-transplant, os was 60%, 45.5%, and 37.5% respectively. Statistically significant differences in os, relapse mortality, and non-relapse mortality were not identified between the prognostic risk groups identified by Michelis and colleagues. Women were less likely than men to relapse, and a modified European Society for Blood and Marrow Transplantation (mebmt) score of 3 or less was associated with a lower non-relapse mortality. CONCLUSIONS: The 37.5% 5-year os in this cohort suggests that, compared with other options, ahsct offers patients with aml in cr2 a better chance of cure. Our study supports the use of the mebmt score to predict non-relapse mortality in this population.
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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 | 0.001 | 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) | 0.001 | 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".