Impact of Conditioning Intensity on Survival in Adult Patients (< 65 Years) With Acute Myeloid Leukemia Receiving Antithymocyte Globulin and Post‐Transplantation Cyclophosphamide Based <scp>GVHD</scp> Prophylaxis
Bibliographic record
Abstract
INTRODUCTION: Myeloablative conditioning (MAC) for acute myeloid leukemia (AML) improves disease control by reducing relapse risk but is associated with higher non-relapse mortality (NRM). Reduced-intensity conditioning (RIC) aims to minimize toxicity but raises concerns about higher relapse rates. This study evaluates the impact of RIC versus MAC in AML patients under 65 years receiving GVHD prophylaxis with antithymocyte globulin, post-transplant cyclophosphamide, and cyclosporine. METHODS: We retrospectively analyzed 322 AML patients undergoing allogeneic HCT with uniform GVHD prophylaxis. Propensity score matching (PSM) was applied to adjust for baseline differences. RESULTS: In the matched cohort, 2-year overall survival (OS) did not differ significantly between RIC and MAC recipients (64.4% vs. 66.9%, p = 0.56). Relapse-free survival (RFS) at 2 years was 65.0% for MAC and 52.7% for RIC (p = 0.20). Two-year NRM was 19.4% for MAC and 19.1% for RIC (p = 0.84). Improved RFS was associated with non-high-risk DRI (HR: 0.39, p = 0.008), whereas conditioning intensity had no significant effect (HR: 0.98, p = 0.97). NRM was higher among patients with KPS < 90 (HR: 3.63, p = 0.01), with no significant impact observed from conditioning intensity (HR: 1.44, p = 0.43). CONCLUSION: In a relatively younger cohort, conditioning intensity did not significantly impact survival, and MAC was not associated with increased NRM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".