Acute myeloid leukaemia relapse after allogeneic haematopoietic stem cell transplantation: Mechanistic diversity and therapeutic directions
Bibliographic record
Abstract
Emerging biological and clinical data, along with advances in new technologies, have exposed the mechanistic diversity in post-haematopoietic stem cell transplant (HCT) relapse. Post-HCT relapse mechanisms are relevant for guiding sophisticated selection of therapeutic interventions and identification of areas for further research. Clonal evolution and emergence of resistant leukemic strains is a common mechanism shared by relapse post-chemotherapy and post-HCT, other mechanisms such as leukemic immune escape and donor T cell exhaustion are unique entities to post-HCT relapse. Due to diversity in the mechanisms behind post-HCT relapse, the subsequent clinical approach relies on clinician discretion, rather than objective evidence. Lack of standardized selection based on post-HCT relapse mechanism(s) could be a contributing factor to observed poor outcomes. Therapeutic strategies including donor lymphocyte infusion (DLI), second transplant, immunotherapies, hypomethylating agents, and targeted strategies are supported options and efficacy may be enhanced when post-HCT AML relapse mechanism is established and guides treatment selection. This review aims, through compilation of supporting studies, to describe mechanisms of post-HCT relapse and their implications for subsequent treatment selection and inspiration for future research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".