Hematopoietic cell transplantation for aplastic anemia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Improvements in allogeneic hematopoietic cell transplantation (HCT) with better donor selection, conditioning regimens and graft vs. host disease prophylaxis make it reasonable to move HCT earlier in the algorithm for management of severe aplastic anemia (SAA). Recent progress in transplantation is reviewed whereas issues related to developing countries are also addressed. RECENT FINDINGS: Multiple research centers are reporting on clonality, mutations and telomere disorders in SAA, which may help to choose the most appropriate therapy upfront. Eltrombopag, in combination with immunosuppressive therapy (IST), has shown remarkable improvement over historical IST, and long-term follow-up is awaited. In younger patients and in experienced centers, matched unrelated-donor (MUD) and related haploidentical transplants (haplo-HCT) are being reported with survival approaching that seen with sibling transplants. Literature from resource-limited countries highlight the need to modify guidelines to make them affordable and cost-effective. Bone marrow remains the graft source of choice; peripheral blood stem cells may be acceptable in special circumstances in resource-constrained countries. SUMMARY: The potential of novel research findings and new therapeutic trials should be maximized by validation in different centers, countries and patient populations to provide personalized care to patients with aplastic anemia.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".