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Record W3162765581 · doi:10.14309/ajg.0000000000001287

Impact of COVID-19 on Prescribing Trends of Direct-Acting Antivirals for the Treatment of Hepatitis C in Ontario, Canada

2021· article· en· W3162765581 on OpenAlexaffabout
Natalia Konstantelos, Ahmad Shakeri, Daniel McCormack, Jordan J. Feld, Tara Gomes, Mina Tadrous

Bibliographic record

VenueThe American Journal of Gastroenterology · 2021
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsSt. Michael's HospitalToronto General HospitalUniversity Health NetworkWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Health careHealthcare systemHepatitis CSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakFamily medicineVirologyEnvironmental healthInternal medicineEconomic growthDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Direct-acting antivirals (DAAs) are curative treatments for hepatitis C. However, initiation of these treatments requires adequate healthcare access. Coronavirus 2019 (COVID-19) resulted in restrictions to healthcare services in March 2020. We examined the impact of COVID-19 on the number of individuals dispensed DAAs. METHODS: This is a cross-sectional study examining the number of individuals dispensed DAAs in Ontario, Canada, from 2018 to 2020. Time-series models determined the impact of healthcare restrictions on DAA dispensations. RESULTS: Healthcare restrictions resulted in a 49.3% decrease in DAA dispensations (P = 0.026). DISCUSSION: COVID-19-related healthcare restrictions significantly affected access to DAAs. Studies exploring the long-term effects on reduced treatment are needed.

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)0.005
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 score0.063
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.356
Teacher spread0.306 · 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".

Quick stats

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe American Journal of GastroenterologySame topicHepatitis C virus researchFrench-language works237,207