Hematopoietic Cell Transplantation in Acute Promyelocytic Leukemia: A Comprehensive Review
Bibliographic record
Abstract
The past three decades have brought major therapeutic advances in the management of acute promyelocytic leukemia. The current state-of-the-art induction treatment with all-trans retinoic acid in combination with anthracycline-based chemotherapy results in long-lasting remissions and cure in up to 70% of newly diagnosed patients. Unfortunately, treatment failure still occurs in one-third of patients. When disease relapses, patients can achieve subsequent remissions with arsenic trioxide, all-trans retinoic acid with or without chemotherapy, or other therapies. Patients achieving molecular remissions after salvage therapy are generally considered candidates for high-dose chemotherapy and autologous hematopoietic cell transplantation as a postconsolidation strategy. On the other hand, patients with evidence of persistent hematologic or molecular disease after salvage therapy could be offered allogeneic hematopoietic transplantation if a suitable HLA-donor is identified and the patient's overall performance and clinical condition are permissible. We hereby provide a comprehensive review and analysis of published clinical trials that evaluate the role of hematopoietic cell transplantation across different stages of acute promyelocytic leukemia.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".