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Record W3033813589 · doi:10.1016/j.aohep.2020.04.011

Granulocyte colony stimulating factor: A potential therapeutic rescue in severe alcoholic hepatitis and decompensated cirrhosis

2020· review· en· W3033813589 on OpenAlexaff
Sahaj Rathi, Trana Hussaini, Eric M. Yoshida

Bibliographic record

VenueAnnals of Hepatology · 2020
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaVancouver General Hospital
Fundersnot available
KeywordsMedicineCirrhosisAlcoholic hepatitisStem cellGranulocyte colony-stimulating factorBone marrowLiver diseaseAlcoholic liver diseaseCytokineHaematopoiesisImmunologyInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

Liver cirrhosis accounts for over 2 million deaths annually worldwide. A subset of these patients - those with alcoholic hepatitis and decompensated cirrhosis, have abysmal short-term survival. Liver transplant is the only intervention of proven survival benefit; however organ availability is a major limitation. It is thus imperative to assess potential benefit of experimental therapies as a bridge to transplant. Stem cell therapies have shown some promise in patients with end-stage liver disease. Of these, bone-marrow derived hematopoietic stem cells have generated the most interest. Animal as well as human data suggest biological plausibility of stem cell translocation from bone marrow to liver, giving credence to cytokine therapies based on bone marrow stimulation. Granulocyte colony stimulating factor has been the most frequently used cytokine for this purpose. This intervention has shown encouraging results in terms of safety as well as survival benefits in small clinical trials. The evidence, however, is sparse and heterogeneous. In this review we describe the biological plausibility, mechanisms of action, and clinical evidence of the use of cytokine based stem cell therapy in patients with end-stage liver disease.

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 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 score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.117
GPT teacher head0.395
Teacher spread0.277 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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