Extramedullary disease at diagnosis of <scp>AML</scp> does not influence outcome of patients undergoing allogeneic hematopoietic cell transplant in <scp>CR</scp>1
Bibliographic record
Abstract
OBJECTIVE: Extramedullary disease (EMD) at diagnosis of acute myeloid leukemia (AML) has been associated with increased risk of relapse and worse outcomes post-chemotherapy. This study sought to investigate the association of EMD with outcomes following allogeneic hematopoietic cell transplantation (allo-HCT). METHODS: This single-center retrospective study investigated the impact of EMD at diagnosis on the outcome of patients transplanted for AML in first complete remission (CR1). The study included 303 consecutive patients with AML transplanted in CR1, median age 51 years (range 18-71). RESULTS: EMD at diagnosis was documented in 39 patients (13%), either histologically (26 patients) or clinically/radiologically (13 patients). Among the 39 EMD patients, 16 had CNS disease, seven had gingival infiltration, and five had leukemia cutis. On univariate analysis, EMD had no significant impact on survival, with a 3-year OS of 55% (95% CI 38-69) compared to 48% for the non-EMD group (95% CI 42%-55%) (P=.84). Likewise, 3-year CIR was 18% vs 19% (P=.86) and 3-year NRM was 26% vs 33% (P=.83) for EMD vs non-EMD groups, respectively. Multivariate analysis confirmed these results. CONCLUSIONS: We conclude that EMD at diagnosis of AML does not seem to influence outcomes following allo-HCT performed in CR1.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".