Risk classification at diagnosis predicts post-HCT outcomes in intermediate-, adverse-risk, and <i>KMT2A</i>-rearranged AML
Bibliographic record
Abstract
Little is known about whether risk classification at diagnosis predicts post-hematopoietic cell transplantation (HCT) outcomes in patients with acute myeloid leukemia (AML). We evaluated 8709 patients with AML from the CIBMTR database, and after selection and manual curation of the cytogenetics data, 3779 patients in first complete remission were included in the final analysis: 2384 with intermediate-risk, 969 with adverse-risk, and 426 with KMT2A-rearranged disease. An adjusted multivariable analysis detected an increased risk of relapse for patients with KMT2A-rearranged or adverse-risk AML as compared to those with intermediate-risk disease (hazards ratio [HR], 1.27; P = .01; HR, 1.71; P < .001, respectively). Leukemia-free survival was similar for patients with KMT2A rearrangement or adverse risk (HR, 1.26; P = .002, and HR, 1.47; P < .001), as was overall survival (HR, 1.32; P < .001, and HR, 1.45; P < .001). No differences in outcome were detected when patients were stratified by KMT2A fusion partner. This study is the largest conducted to date on post-HCT outcomes in AML, with manually curated cytogenetics used for risk stratification. Our work demonstrates that risk classification at diagnosis remains predictive of post-HCT outcomes in AML. It also highlights the critical need to develop novel treatment strategies for patients with KMT2A-rearranged and adverse-risk disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".