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Record W4213374557 · doi:10.1093/jcag/gwab049.213

A214 SIMPLIFICATION OF CARE FOR HCV IS EFFECTIVE DURING THE COVID PANDEMIC: A RETROSPECTIVE STUDY OF HCV TREATMENT UTILIZING THE BRITISH COLUMBIA HEPATITIS C NETWORK

2022· article· en· W4213374557 on OpenAlexaffabout
S X Jiang, Jeanette F. Farivar, Julia L. MacIsaac, Eric Tam, Min Hyuk Choi, P Luyun, Hin Hin Ko, Alnoor Ramji

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsCanadian Society of Intestinal ResearchUniversity of British Columbia
Fundersnot available
KeywordsPandemicMedicineRegimenRetrospective cohort studyHepatitis C virusHepatitis CCoronavirus disease 2019 (COVID-19)Internal medicinePediatricsVirologyVirusDisease

Abstract

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Abstract Background The COVID-19 pandemic has impacted healthcare access, including to curative treatment for hepatitis C (HCV) infection in the form of direct-acting antivirals (DAAs). A 49% decrease in DAA dispensations in Canada during the pandemic has been reported, but little is known about these treated populations. Aims To explore the patient characteristics and treatment patterns in those who were treated for HCV during the COVID pandemic. Methods A retrospective chart review was conducted at one site of utilizing the British Columbia Hepatitis C Network. Only patients included into the database were analyzed. Patients started on treatment between 03/17/2020-03/16/2021 were included as the “pandemic group” and patients from the 03/17/2019-03/16/2020 were included as a comparison “pre-pandemic group”. Data were extracted for clinicodemographic variables, laboratory investigations, treatment start date, regimen, and sustained virologic response at 12 weeks (SVR12). Results 97 patients were treated during the pandemic compared to 143 patients the year prior, representing a 32% decline. Patients treated during the pandemic were predominantly new referrals (n=70, 72% vs n=64, 45% pre-pandemic, p<0.01) and had fewer total appointments (median 2 per patient vs 4 per patient pre-pandemic, p<0.01). There was a median of 1 in-person visit and 1 telehealth appointment per patient during the pandemic (vs median 2 per patient of each type pre-pandemic). Pandemic patients were younger (mean age 56.0 years vs 59.6 pre-pandemic, p=0.04), and a greater proportion were on opioid agonist therapy (28% vs 13% pre-pandemic, p<0.01). Less transient elastography (TE) was performed during the pandemic (69% vs 89% pre-pandemic). Amongst those with TE scores, a lower proportion of those treated during the pandemic were cirrhotic (13% vs 21% pre-pandemic). During the pandemic, treatment patterns shifted towards more prescriptions for glecaprevir/pibrentasvir (56% of all prescriptions vs 44% pre-pandemic) and sofosbuvir/velpatasvir (37% vs 29% pre-pandemic). There was slightly less use of sofosbuvir/velpatasvir/voxilaprevir at (2% vs 4% pre-pandemic). The proportion of patients who completed lab work for SVR was similar during the pandemic (n=83/97, 85.6%) compared to pre-pandemic (n=120/143, 83.9%). Similarly, SVR12 remained high during the pandemic at 98.7% (vs 99.3% pre-pandemic). Of all 97 patients prescribed DAAs during the pandemic, 92 (94.8%) completed treatment. Conclusions Less persons were treated during the COVID pandemic, which may deter progress towards HCV elimination targets. Very high SVR12 and treatment completion rates during the pandemic suggest that patients can be effectively treated with less pre-treatment investigations and fewer appointments. Funding Agencies None

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.923

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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