MétaCan
Menu
Back to cohort
Record W2748618284 · doi:10.1021/acsinfecdis.7b00091

Entry Inhibitors: A Perspective for Prevention of Hepatitis C Virus Infection in Organ Transplantation

2017· article· en· W2748618284 on OpenAlexfundno aff
Che C. Colpitts, Raymond T. Chung, Thomas F. Baumert

Bibliographic record

VenueACS Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
FundersFP7 Ideas: European Research CouncilNational Institute of Diabetes and Digestive and Kidney DiseasesH2020 European Research CouncilNational Institute on Drug AbuseAgence Nationale de la RechercheAssociation pour la Recherche sur le CancerNational Institute of Allergy and Infectious DiseasesInstitute of Infection and ImmunityCanadian Institutes of Health Research
KeywordsOrgan transplantationHepatitis C virusMedicineContext (archaeology)TransplantationEconomic shortageImmunologyHepatitis CViral entryVirusVirologyLiver transplantationDiseaseViral replicationBiologyInternal medicine

Abstract

fetched live from OpenAlex

Entry inhibitors are emerging as an attractive class of therapeutics for hepatitis C virus (HCV) infection. Entry inhibitors target either virion-associated factors or cellular factors necessary for infection. By blocking entry into cells, entry inhibitors prevent both the establishment of persistent reservoirs and the emergence of resistant variants during viral replication. Furthermore, entry inhibitors protect naïve cells from virus-induced alterations. Combining entry inhibitors with direct-acting antivirals (DAAs) may therefore improve treatment outcomes, particularly in the context of organ transplantation. The role of DAAs in transplantation, while still under clinical investigation, carries the risk of recipient infection and HCV-induced disease, since DAAs act only after infection is established. Thus, entry inhibitors provide a perspective to improve patient outcomes during organ transplantation. Applying this approach for transplant of organs from HCV-positive donors to HCV-negative recipients may also contribute to alleviate the medical burden of organ shortage.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.022
GPT teacher head0.355
Teacher spread0.333 · 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 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueACS Infectious DiseasesSame topicHepatitis C virus researchFrench-language works237,207