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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".