The prognostic impact of reduced variant burden in elderly patients with acute myeloid leukemia treated with decitabine
Bibliographic record
Abstract
BACKGROUND/AIMS: We evaluated the role of next-generation sequencing (NGS)-based disease monitoring for elderly patients diagnosed with acute myeloid leukemia (AML) who received decitabine therapy. METHODS: A total of 123 patients aged > 65 years with AML who received decitabine were eligible. We analyzed the dynamics of variant allele frequency (VAF) in 49 available follow-up samples after the fourth cycle of decitabine. The 58.6% VAF clearance (Δ, [VAF at diagnosis - VAF at follow-up] × 100 / VAF at diagnosis) was the optimal cut-off for predicting overall survival (OS). RESULTS: The overall response rate was 34.1% (eight patients with complete remission [CR], six of CR with incomplete hematologic recovery, 22 with partial responses, and six with morphologic leukemia-free status). Responders (n = 42) had significantly better OS compared with non-responders (n = 42) (median, 15.3 months vs. 6.5 months; p < 0.001). Of the 49 patients available for follow-up targeted NGS analysis, 44 had trackable gene mutations. The median OS of patients with ΔVAF ≥ 58.6% (n=24) was significantly better than that of patients with ΔVAF < 58.6% (n = 19) (20.5 months vs. 9.8 months, p = 0.010). Moreover, responders with ΔVAF ≥ 58.6% (n = 20) had a significantly longer median OS compared with responders with VAF < 58.6% (n = 11) (22.5 months vs. 9.8 months, p = 0.004). CONCLUSION: This study suggested that combining ΔVAF ≥ 58.6%, a molecular response, with morphologic and hematologic responses can more accurately predict OS in elderly AML patients after decitabine therapy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".