Clinical perspectives on post‐induction maintenance therapy in patients with acute myeloid leukaemia in remission who are ineligible for allogeneic haematopoietic stem cell transplantation
Bibliographic record
Abstract
For patients with acute myeloid leukaemia (AML) who achieve complete remission (CR) after induction therapy, subsequent allogeneic haematopoietic stem cell transplantation (allo-HSCT) reduces the risk of relapse. However, not all patients are eligible, warranting effective alternative maintenance strategies. Oral azacitidine is the only non-targeted therapy approved by both the United States (US) Food and Drug Administration and the European Medicines Agency for the maintenance or continued treatment of allo-HSCT-ineligible patients with AML achieving CR or CR with incomplete haematological recovery following induction chemotherapy. Midostaurin and histamine dihydrochloride are approved in Europe as maintenance therapy for AML in remission, and quizartinib is approved in the United States and Europe for the treatment and maintenance of patients with newly diagnosed FLT3-ITD AML. Barriers to maintenance treatment include limited clinical trial data informing appropriate patient and treatment selection, patient preference, financial burden and paucity of real-world data. This article discusses current maintenance treatment guidelines for patients with AML in remission but not proceeding to allo-HSCT and reviews clinical trial data for agents approved for use in remission. Ongoing studies of interest and considerations for future efforts are also discussed.
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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 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".