Simplified treatment of hepatitis C during the COVID-19 pandemic: retrospective analysis of the British Columbia Hepatitis C Network
Bibliographic record
Abstract
Background: The COVID-19 pandemic changed the landscape of hepatitis C virus (HCV) treatment in Canada. In this study, we sought to describe the characteristics, management, and outcomes of patients treated during the pandemic. Methods: Retrospective analysis of the British Columbia HCV Network included HCV patients treated from March 17, 2018 to February 22, 2022. Patients who started treatment before and after March 17, 2020 were designated pre-pandemic and pandemic groups, respectively. Patients were followed until sustained virologic response 12 weeks post-treatment (SVR12). Results: A total of 851 patients underwent 854 treatments, with 481 (56%) pre-pandemic and 373 (44%) pandemic. Pandemic patients were younger (median age 57 versus 61 pre-pandemic; p <0.01) and 23% were on opioid agonist therapy (versus 11% pre-pandemic; p = 0.01). Fewer pandemic patients completed transient elastography (36% versus 56% pre-pandemic; p < 0.01). Pandemic patients utilized fewer in-person appointments and more telehealth appointments ( p < 0.01). Fewer pandemic patients completed treatment (85% versus 91% pre-pandemic; p = 0.23); the SVR12 rate was 97.8% in those completing treatment and lab work (versus 99.5% pre-pandemic; p < 0.01). Younger age, substance use, and opioid agonist therapy were associated with loss to follow-up during the pandemic. Conclusions: Patients treated for HCV in British Columbia during the pandemic utilized fewer resources and had more loss to follow-up but maintained high SVR12 rates. Transitioning from in-person to telehealth appointments proved effective in a real-world setting. Individualized strategies are required for special populations prone to loss to follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".