Eltrombopag after allogeneic haematopoietic cell transplantation in a case of poor graft function and systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Late graft failure after allogeneic haematopoietic cell transplantation (HCT) can result from the failed engraftment of long-term engrafting cells. The use of thrombopoietin (TPO) receptor agonists (TRA) has been extensively studied and remains an important component of experimental ex vivo stem cell expansion protocols, but its use in allogeneic transplantation is still evolving. METHODS: We describe the use of eltrombopag, a TRA, to stimulate the rescue of late graft failure in a patient following allogeneic HCT, and we performed a systematic review of published studies describing the use of TRAs following allogeneic transplantation. RESULTS: A total of eight publications were identified from our systematic search and included observational case studies (five studies, total of seven patients) that primarily addressed ITP or isolated thrombocytopenia at various time points after allogeneic HCT and prospective clinical trials (three studies, total of 177 patients with 95 patients receiving TRAs). No studies reported specifically on the use of TRAs for the treatment of trilineage graft failure as a means of in vivo stem cell expansion. The use of TRAs following allogeneic HCT appears safe and promising. CONCLUSION: The use of eltrombopag or other TRAs to treat poor graft function after allogeneic HCT is intriguing and warrants further study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.003 | 0.000 |
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".