Prognostic utility of six mutated genes for older patients with acute myeloid leukemia
Bibliographic record
Abstract
Approximately 50% of older patients with acute myeloid leukemia (AML) do not obtain chromosomal abnormalities as an effective risk-stratification, and present cytogenetically normal AML (CN-AML). To develop a reliable prediction model for stratifying the risk of these elderly patients, we conducted a study with a discovery and validation design. As a result, we found the top 6 mutated genes in the discovery cohort of 26 case by the whole exome sequencing, and verified as recurrent mutations in the large cohort of 329 patients by Sanger sequencing. The top 6 genes were NPM1, FLT3-ITD, DNMT3A, CEBPA double allele, IDH1 and IDH2 mutations, and the frequency of each gene in the combining cohort was 36.8%, 19.8%, 20.1%, 5.8%, 14.9% and 22.5%, respectively. In addition, clinical variables such as age, white blood cell counts, genes of IDH1 and DNMT3A mutations, European LeukemiaNet genotype (NPM1 mutations and lacking FLT3-ITD or CEBPA double allele mutations) and treatment protocols were independent factors for predicting the probabilities of overall and event-free survival. The prediction nomograms based on these significant factors showed accurate discrimination. In conclusion, we developed a reliable prediction model for stratifying the risk of elderly patients with CN-AML.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".