Classifying Cytogenetics in Patients with Acute Myelogenous Leukemia in Complete Remission Undergoing Allogeneic Transplantation: A Center for International Blood and Marrow Transplant Research Study
Bibliographic record
Abstract
Cytogenetics play a major role in determining the prognosis of patients with acute myelogenous leukemia (AML). However, existing cytogenetics classifications were developed in chemotherapy-treated patients and might not be optimal for patients undergoing allogeneic hematopoietic cell transplantation (HCT). We studied 821 adult patients reported to the Center for International Blood and Marrow Transplant Research (CIBMTR) who underwent HCT for AML in first or second complete remission between 1999 and 2004. We compared the ability of the 6 existing classifications to stratify patients by overall survival. We then defined a new scheme specifically applicable to patients undergoing HCT using this patient cohort. Under this scheme, inv(16) is favorable, a complex karyotype (4 or more abnormalities) is adverse, and all other classified abnormalities are intermediate in predicting survival after HCT (5-year overall survival, 64%, 18%, and 50%, respectively; P = .0001). This scheme stratifies patients into 3 groups with similar nonrelapse mortality, but significantly different incidences of relapse, overall and leukemia-free survival. It applies to patients regardless of disease status (first or second complete remission), donor type (matched related or unrelated), or conditioning intensity (myeloablative or reduced intensity). This transplantation-specific classification could be adopted for prognostication purposes and to stratify patients with AML and karyotypic abnormalities entering HCT clinical trials.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".