Combination of FLT3-ITD Allelic Ratio, NPM1 Mutation, and Immunophenotypic Markers to Modulate Outcome Prediction in Patients with Normal Karyotype Acute Myelogenous Leukemia Undergoing Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
NPM1 mutation status and the allelic ratio (AR) of FLT3- internal tandem duplication (FLT3-ITD) are routinely tested for disease risk stratification in patients with normal karyotype (NK) acute myelogenous leukemia (AML); however, the predictive impact of immunophenotypic markers on different NPM1/FLT3 genotypes remains unclear. We performed a retrospective analysis of 423 patients with NK-AML subclassified into groups based on NPM1/FLT3 genotype. Allogeneic hematopoietic stem cell transplantation (HSCT) was performed in 124 of 423 patients (29%) and was significantly associated with longer event-free survival (EFS) and overall survival (OS), except for patients with the favorable genotype, defined as mutated NPM1 ( NPM1 mut ) combined with normal FLT3 status ( FLT3 -ITD neg ) or FLT3 -ITD AR <.5 ( FLT3 -ITD low ). A subset of AML patients bearing the favorable NPM1 mut / FLT3 -ITD neg/low genotype share similar outcomes with AML patients who have the intermediate FLT3/NPM1 genotype defined by normal NPM1 ( NPM1 wt ) and FLT3 -ITD neg/low . In these individuals, the lack of CD13 expression (CD13 neg ) was associated with shorter EFS ( P = .041) and OS ( P = .017). CD13 neg was an independent predictor for shorter OS (hazard ratio, 1.985; P = .028).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".