MétaCan
Menu
Back to cohort
Record W4224287100 · doi:10.2217/fvl-2022-0016

Real-world Effectiveness of sofosbuvir/velpatasvir for the Treatment of Hepatitis C Virus in Prison Settings

2022· article· en· W4224287100 on OpenAlexaffabout
Silvia Rosati, Alexander Wong, V. Di Marco, Paco Pérez-Hernandez, Guilherme Macedo, Christian Brixko, Roberto Ranieri, Francesca Campanale, Annalisa Basciá, Conrado Fernández‐Rodríguez, Victor de Lédinghen, Ivana Maida, Elisabetta Teti, Alessandra Mangia, Kim Vanstraelen, Cándido Hernández, Michael Mertens, Ιωάννα Ντάλλα, Heribert Ramroth, Elena Garza Jiménez

Bibliographic record

VenueFuture Virology · 2022
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of Saskatchewan
FundersGilead Sciences
KeywordsPrisonSofosbuvirMedicineGuidelineHepatitis C virusHepatitis CMental healthPsychiatryPopulationInternal medicineRibavirinVirologyVirusPsychologyEnvironmental healthPathology

Abstract

fetched live from OpenAlex

Background: People in prison are at high risk of hepatitis C virus (HCV) infection and often have a history of injection drug use and mental health disorders. Simple test-and-treat regimens which require minimal monitoring are critical. Methods: This integrated real-world analysis evaluated the effectiveness of once daily sofosbuvir/velpatasvir (SOF/VEL) in 20 prison cohorts across Europe and Canada. The primary outcome was sustained virological response (SVR) in the effectiveness population (EP), defined as patients with a valid SVR status. Secondary outcomes were reasons for not achieving SVR, adherence and time between HCV RNA diagnosis and SOF/VEL treatment. Results: Overall, 526 people in prison were included with 98.9% SVR achieved in the EP (n = 442). Cure rates were not compromised by drug use or existence of mental health disorders. Conclusion: SOF/VEL for 12 weeks is highly successful in prison settings and enables the implementation of a simple treatment algorithm in line with guideline recommendations and test-and-treat strategies.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.223
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.333
Teacher spread0.314 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFuture VirologySame topicHepatitis C virus researchFrench-language works237,207