Evidence map›Paper›PMID 38835674›Full record

ArticleFederal practitioner : for the health care professionals of the VA, DoD, and PHS2024

Outcomes and Barriers Associated with Telehealth-Based Hepatitis C Treatment During Early Phases of the COVID-19 Pandemic.

Phillip Huang Chen, Justin Truong, Jenna Kawamoto, Debika Bhattacharya, Arpan Patel

Abstract read
In one paragraph

Article in Federal practitioner : for the health care professionals of the VA, DoD, and PHS, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Phillip Huang ChenDavid Geffen School of Medicine, University of California, Los Angeles.
Justin TruongVeterans Affairs Greater Los Angeles Healthcare System.
Jenna KawamotoVeterans Affairs Greater Los Angeles Healthcare System.
Debika BhattacharyaDavid Geffen School of Medicine, University of California, Los Angeles.
Arpan PatelVeterans Affairs Greater Los Angeles Healthcare System.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The COVID-19 pandemic has presented challenges for hepatitis C virus (HCV) treatment given the need for thorough evaluation by specialists, treatment coordination, follow-up visits, laboratory monitoring, and potential health behavior impacts on patients. The objective of this study was to evaluate HCV treatment during the beginning of the COVID-19 pandemic, when care was conducted virtually, by examining patient demographics associated with treatment initiation and discontinuation rates. Methods: This retrospective study included 73 patients with quantifiable HCV RNA evaluated by gastroenterologists and infectious disease clinicians and referred to an HCV clinical pharmacy team for treatment coordination from March 1, 2020, to September 30, 2020. Data collection included baseline demographics, clinical characteristics, and treatment characteristics. Patients were followed until June 15, 2021. Results: Forty-three patients (59%) initiated HCV treatment while 30 patients (41%) did not. Patient demographics were not associated with HCV treatment initiation rates except for presence of alcohol use disorder within the past 6 months ( Conclusions: Results of this study suggest that active alcohol use disorder diagnosis may be associated with HCV treatment noninitiation. This study emphasizes the need for further research to define the standards of care in assessing active alcohol use disorder during HCV treatment evaluation.

Identifiers

PMID38835674
PMCPMC11147438

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.