MétaCan
Menu
Back to cohort
Record W2164172064 · doi:10.20882/adicciones.698

Hepatitis C asociada al abuso de sustancias: nunca tan cerca de un tratamiento sin Interferón

2015· article· es· W2164172064 on OpenAlex
Roberto Muga, Paola Zuluaga, Arantza Sanvisens, Inmaculada Rivas, Daniel Fuster, Ferrán Bolao, Jordi Tor, Red de Trastornos Adictivos-RTA

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdicciones · 2015
Typearticle
Languagees
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHepatitis C virusMedicineHepatitis CLiver diseaseVirologyImmunologyVirusInternal medicine

Abstract

fetched live from OpenAlex

With 3-4 million of new infections occurring annually, hepatitis C virus (HCV) infection is a global Public Health problem. In fact, hepatitis C virus infection is one of the leading causes of liver disease in the world; in Western countries, two thirds of the new HCV infections are associated with injection drug use. The treatment of hepatitis C will change in the coming years with the irruption of new anti-HCV drugs, the so called Direct Antiviral Agents (DAA) that attack key proteins of the HCV life cycle. The new antiviral drugs are effective, safer and better tolerated. The 2014 WHO HCV treatment guidelines include some of them. The new DAA are used in combination and it is expected that Interferon will be not necessary in future treatment regimens against HCV infection. The irruption of new and potent antivirals mandate the review of the current standards of care in the HCV infected population. More inclusive and proactive treatment policies will be necessary in those individuals with substance use disorders.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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