Proposed Refinement of 2022 European LeukemiaNet Adverse-Risk Group of AML Patients Using a Real-World Cohort
Bibliographic record
Abstract
Background/Objectives: The 2022 European LeukemiaNet (ELN 2022) is a widely used genotypic risk classification tool for the treatment and prognostication of acute myeloid leukemia (AML) patients. Our study evaluates its effectiveness in categorizing adverse-risk AML patients on standard therapy based on their overall survival (OS). Methods: We conducted a retrospective study involving 256 AML patients. Results: Those in the ELN 2022 adverse-risk group had the shortest OS (p < 0.0001) and were predominantly characterized by myelodysplasia-related (MR) mutations, complex karyotype (CK), monosomal karyotype (MK), and TP53 mutation (TP53 Mut). Subclassification and analysis of this adverse-risk group based on the TP53 Mut status revealed a significantly shorter OS compared to the adverse TP53 wild-type (TP53 WT) counterparts (p = 0.0036). We propose refining the ELN 2022 adverse-risk group into two categories, adverse TP53 Mut and adverse TP53 WT groups, to represent adverse- and ultra-adverse-risk groups, respectively. We used an external validation dataset to confirm our findings. Conclusions: This refinement allows for a more accurate classification of these adverse-risk patients based on their clinical outcomes.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".