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Record W2146030280 · doi:10.1007/s00534-010-0305-1

Autologous bone marrow cell infusion therapy for liver cirrhosis patients

2010· article· en· W2146030280 on OpenAlexfundno aff
Shuji Terai, Isao Sakaida

Bibliographic record

VenueJournal of Hepato-Biliary-Pancreatic Sciences · 2010
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceAlberta Biodiversity Monitoring Institute
KeywordsCirrhosisMedicineBone marrowLiver functionBone marrow suppressionInternal medicineLiver function testsLiver cellGastroenterologyChemotherapy

Abstract

fetched live from OpenAlex

We developed a novel cell therapy, autologous bone marrow cell infusion (ABMi) therapy, using autologous bone marrow, for liver cirrhosis patients. Our study depends on the findings from basic studies that bone marrow cell infusion repairs liver fibrosis in the cirrhotic liver, and improves liver function and the survival rate. Beginning in November 2003, we started a clinical study and found that ABMi therapy was safe and effective for liver cirrhosis patients. Multicenter trials in Japan and Korea have also shown the effectiveness of ABMi therapy. In this review, we report the current status of ABMi therapy for liver cirrhosis patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.019
GPT teacher head0.274
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations13
Published2010
Admission routes1
Has abstractyes

Explore more

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