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Record W2795903628 · doi:10.1002/lt.25072

Should organs from hepatitis C‐positive donors be used in hepatitis C‐negative recipients for liver transplantation?

2018· review· en· W2795903628 on OpenAlexaff
Nazia Selzner, Marina Berenguer

Bibliographic record

VenueLiver Transplantation · 2018
Typereview
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineLiver transplantationHepatitis C virusHepatitis CTransplantationIntensive care medicineTransmission (telecommunications)Liver diseaseScarcityInternal medicineImmunologyVirus

Abstract

fetched live from OpenAlex

Given the scarcity of donated organs and the frequency of death on the waiting list, strategies that could improve the available supply of high-quality liver grafts are much needed. Direct-acting antiviral agent (DAA) regimens have proved to be highly effective to treat hepatitis C virus (HCV), even in the setting of posttransplantation. The question arises as to whether transplant communities should consider the utilization of HCV-positive donors into HCV-negative recipients. This review summarizes risk of transmission, treatment options with success rate, and ethical considerations for usage of HCV-positive donors. Liver Transplantation 24 831-840 2018 AASLD.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.104
GPT teacher head0.378
Teacher spread0.274 · 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 designNot applicable
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

Citations39
Published2018
Admission routes1
Has abstractyes

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