Allogeneic Hematopoietic Cell Transplantation May Alleviate the Negative Prognostic Impact of Monosomal and Complex Karyotypes on Patients with Acute Myeloid Leukemia
Bibliographic record
Abstract
Monosomal karyotype (MK) and complex karyotype (CK) are well known to be associated with a very poor clinical outcome in patients with acute myeloid leukemia (AML). However, whether or not the prognostic impact of MK and CK remains relevant for patients who have undergone allogeneic hematopoietic cell transplantation (allo-HCT) is still unclear. We retrospectively analyzed the status of MK and CK, as well as other clinical laboratory features, in 148 allo-HCT AML patients at our institution and correlated with their event-free survival (EFS) and overall survival (OS) after transplantation. MK and CK were identified in 14 (9%) and 19 (13%) cases, respectively. On univariate analysis, only age (≥60 years) and WBC count (≥15 × 10(9)/L) were significant adverse predictors for EFS (P < .001 and P = .017, respectively) and OS (P = .002 and P = .021, respectively). MK, CK, and other relevant parameters analyzed did not affect the clinical outcome. Multivariable analysis confirmed that both older age and high WBC count were independent prognostic factors for a shorter OS (P = .001 and P = .003, respectively) and a shorter EFS (P < .001 and P = .001, respectively). Our results indicate that neither MK nor CK are high-risk factors in AML patients undergoing allo-HCT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".