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Record W2335279309 · doi:10.1517/14728214.2016.1170117

Emerging drugs for graft-versus-host disease

2016· review· en· W2335279309 on OpenAlexaff
Natasha Kekre, Joseph H. Antin

Bibliographic record

VenueExpert Opinion on Emerging Drugs · 2016
Typereview
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineDiseaseClinical trialGraft-versus-host diseaseImmunologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Graft-versus-host disease (GVHD) leads to significant morbidity and mortality after allogeneic stem cell transplantation. While corticosteroids alone are adequate in some cases, they are often insufficient, leading to poor quality of life associated with the symptoms of disease, or mortality from infection and GVHD. Moreover, corticosteroids have significant side effects and often do not lead to durable responses. New therapies are needed to improve the development and progression of acute and chronic GVHD. AREAS COVERED: We discuss the spectrum of emerging drugs for GVHD prevention and therapy. Cellular therapies will be briefly discussed. The available pre-clinical and clinical data regarding monoclonal antibodies, interleukin-2, alpha-1 antitrypsin, histone deacetylase inhibitors, tyrosine kinase inhibitors, and proteasome inhibitors will be reviewed. EXPERT OPINION: Although therapies emerging for GVHD remain promising, most of these drugs are still in early phase clinical trials and require randomized comparisons before formal conclusions can be drawn. It is likely that in the near future some of these agents will show improvements in response when compared with corticosteroids alone. Although it is difficult to predict which of these agents will be most promising, alpha-1 antitrypsin, ruxolitinib and interleukin-2 have demonstrated encouraging results.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.055
GPT teacher head0.393
Teacher spread0.338 · 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.

Study designOther design
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

Citations11
Published2016
Admission routes1
Has abstractyes

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