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Record W2336834983 · doi:10.1111/tme.12300

Eltrombopag after allogeneic haematopoietic cell transplantation in a case of poor graft function and systematic review of the literature

2016· review· en· W2336834983 on OpenAlexaff
J. Dyba, Alan Tinmouth, Christopher Bredeson, John Matthews, David Allan

Bibliographic record

VenueTransfusion Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsOttawa HospitalQueen's UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicineEltrombopagTransplantationHaematopoiesisStem cellSurgeryThrombopoietin receptorClinical trialThrombopoietinInternal medicinePlateletBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.280
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations33
Published2016
Admission routes1
Has abstractyes

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