Allogeneic hematopoietic stem cell transplantation in adult with acute lymphoblastic leukemia: evolving indications and modalities in shifting landscape
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) has been a cornerstone in the treatment of adult acute lymphoblastic leukemia (ALL). Its indications have evolved with the adoption of pediatric-inspired protocols, refined risk stratification based on minimal residual disease (MRD), the identification of high-risk genetic subtypes, and the emergence of novel immunotherapies. Agents such as blinatumomab and inotuzumab ozogamicin can induce deep remissions and increasingly challenge traditional transplant algorithms. Chimeric antigen receptor T cell (CAR T-cell) therapies further reshape post-relapse strategies, while advances in conditioning regimens and donor selection have broadened allo-HSCT applicability. Current evidence supports allo-HSCT in patients with high-risk features or persistent MRD, though its benefit is increasingly debated in MRD-negative responders. This review synthesizes evolving data on indications, timing, modalities, and outcomes of allo-HSCT in adult ALL and highlights the need for personalized, MRD and genomics-guided approaches to optimize cure while minimizing transplant-related risks in the immunotherapy era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".