Prognostic analysis according to European LeukemiaNet 2022 risk stratification for elderly patients with acute myeloid leukemia treated with decitabine
Bibliographic record
Abstract
Objectives This study aimed to evaluate the prognostic significance of the revised European LeukemiaNet (ELN)−2022 risk stratification model for 123 elderly acute myeloid leukemia (AML) patients treated with decitabine chemotherapy.Results Based on the ELN-2022 risk stratification, 15 (12.2%), 51 (41.5%), and 57 (46.3%) patients were classified as having favorable, intermediate, and high-risk AML, respectively. In comparison with the ELN-2017 risk stratification, the ELN-2022 risk stratification re-assigned 26 (21.1%) and three (2.4%) patients to the adverse and favorable risk groups, respectively. Survival analysis revealed distinctive overall survival (OS) outcomes among the ELN-2022 risk groups (6-month OS rate: 73.3%, 52.9%, and 47.7% for favorable, intermediate, and adverse risk, respectively; P = 0.101), with a parallel trend observed in the event-free survival (EFS) (6-month EFS rate: 73.3%, 52.9%, and 45.6% for favorable, intermediate, and adverse risk, respectively; P = 0.049). Notably, both OS and EFS in the favorable risk group were significantly superior in comparison to that of the adverse risk group (OS: P = 0.040, EFS: P = 0.030). Although the ELN-2022 C-index (0.559) was greater than the ELN-2017 C-index (0.539), the result was not statistically significant (P = 0.059). Based on the event net reclassification index, we consistently observed significant improvements in the ELN-2022 risk stratification for overall survival (0.21 at 6 months).Conclusion In conclusion, the revised ELN-2022 risk stratification model may have improved the risk classification of elderly AML patients treated with hypomethylating agents compared to the ELN-2017 risk stratification model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".