Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (allo-HCT) remains a cornerstone in the treatment of high-risk acute lymphoblastic leukemia (ALL), yet optimal patient selection is challenging in the era of rapidly changing modern therapy. Refined molecular characterization allows for better risk assessment, sparing low-risk patients from allo-HCT toxicity while identifying those who may benefit from intensified approaches. Measurable residual disease (MRD) has emerged as a powerful predictor of relapse irrespective of treatment strategy, challenging the necessity of transplant in MRD-negative patients. Further, expanded donor options, particularly haploidentical transplantation coupled with reduced intensity conditioning, have extended the applicability of allo-HCT to a broader range of patients. Finally, immunotherapies and targeted treatments are increasingly integrated into both initial and relapsed treatment protocols yielding deep remission and allowing for successful transplant in patients with a history of advanced disease. In this review, we provide an overview of the contemporary role of transplant in adult patients with ALL, focusing on indications for allo-HCT in first remission, optimal sequencing of transplant with novel therapies, and advancements in donor selection and conditioning regimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".