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Record W4308184764 · doi:10.1002/hep4.2101

Leveraging corona virus disease 2019 vaccination to promote hepatitis C screening

2022· article· en· W4308184764 on OpenAlexaff
Aaron Vanderhoff, David Smookler, Mia J. Biondi, Scott Enman, Tintin Fuliang, Sana Mahmood, Agustina Crespi, María Elena Márquez, Rafique Van Uum, Lucy You, Brett Wolfson‐Stofko, Renee Logan, Erin LeDrew, Hemant Shah, Harry L.A. Janssen, Camelia Capraru, Elisa Venier, Jordan J. Feld

Bibliographic record

VenueHepatology Communications · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsVaccinationHepatitis C virusMedicinePandemicDiseaseVirologyCoronavirus disease 2019 (COVID-19)VirusAntibodyTest (biology)ImmunologyHealth careFamily medicineInternal medicineInfectious disease (medical specialty)Biology

Abstract

fetched live from OpenAlex

Health care initiatives, such as hepatitis C virus (HCV) screening, have been greatly overshadowed by the corona virus disease 2019 (COVID-19) pandemic. However, COVID-19 vaccination programs also provide an opportunity to engage with a high volume of people in a health care setting. We collaborated with a large COVID vaccination center to offer HCV point-of-care testing followed by dried blood spot collection for HCV RNA. Additionally, this opportunity was used to evaluate the practical significance of a 5-minute version of the OraQuick HCV antibody test in lieu of the standard 20-minute test. We tested 2317 individuals; 31 were HCV antibody positive and six were RNA positive of which four were treated and reached sustained virological response. Over a third of those surveyed said they would not have participated had the test required 20 minutes. Conclusion : Colocalizing HCV testing and linkage to care at a COVID vaccination clinic was found to be highly feasible; furthermore, a shortened antibody test greatly improves the acceptance of testing.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.069
GPT teacher head0.374
Teacher spread0.305 · 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 routes1
Has abstractyes

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