Advances in Allogeneic Hematopoietic Stem Cell Transplantation for Acute Leukemia
Bibliographic record
Abstract
In acute leukemia, advances have been made in therapeutic strategies centered on allogeneic hematopoietic stem cell transplantation (allo-SCT), three of which are presented here.The indication of allo-SCT for acute myeloid leukemia (AML) in 1 st complete remission (CR1) has been debated.Genomic medicine has helped us gain a deeper understanding of this disease, some of which may serve as prognostic factors.Such genetic abnormalities could also help measure minimal residual disease (MRD) and provide additional clues to estimate the efficacy of chemotherapy.Combined with existing prognostic factors, these data can be used to construct a more accurate prognostic model, providing an optimal indication of allo-SCT for AML in CR1.Furthermore, overall treatment algorithms for high-risk AML after allo-SCT should include prophylactic and pre-emptive treatment to prevent relapse.These include immunotherapy using donor lymphocyte infusion (DLI), FLT3 inhibitors in FLT3-mutated AML, hypomethylating agents, or a combination of DLI with these agents.Clinical trials are currently ongoing to elucidate the role of these strategies, which will lead to a risk-adapted treatment for preventing relapse in high-risk AML.CD19-targeted chimeric antigen receptor (CAR) T-cell therapy induces a remarkable response in B-acute lymphoid leukemia (B-ALL); however, relapse remains a major problem.In this regard, allo-SCT as a consolidation treatment after CAR-T cell therapy for B-ALL is recommended for pediatric and adult patients.Achieving complete remission (CR) with CAR-T cell therapy is considered a promising bridging therapy to allo-SCT.Novel CAR-T treatment techniques are being developed to change their role as a pretransplant treatment.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".