Bibliographic record
Abstract
<div><p>Background</p><p>Hepatitis C virus (HCV) has high global prevalence and can lead to liver complications and death. Access to direct-acting antivirals (DAAs) in Canada increased following several policy changes, however the real-world impact of expanded DAA access and increased use of these drugs is unknown.</p><p>Objective</p><p>We aimed to determine the early change in rates of HCV-related hospitalizations overall and HCV-related hospitalizations with hepatocellular carcinoma (HCC) after expanded DAA access.</p><p>Methods</p><p>We conducted a population-based time series analysis using national administrative health databases in Canada. Rates of HCV-related hospitalizations and HCV-related hospitalizations with HCC were enumerated monthly between April 2006 and March 2020. We used Autoregressive Integrated Moving Average (ARIMA) models with ramp functions in October 2014 and January 2017 to evaluate the impact of policies to expand DAA access on hospitalization outcomes.</p><p>Results</p><p>Rates of HCV-related hospitalizations in Canada increased between 2006 and 2014, and gradually declined thereafter. The decrease after October 2014, or the first policy change, was significant (p = 0.0355), but no further change was found after the second policy change in 2017 (p = 0.2567). HCV-related hospitalizations with HCC increased until end of 2013, followed by a plateau, before declining in 2016. No significant shifts were found after the first policy change in 2014 (p = 0.1291) nor the second policy change in 2017 (p = 0.6324). Subgroup analyses revealed that those aged 50–64 and males had observable declines in rates of HCV-related hospitalizations in the year prior to the first policy change.</p><p>Conclusions</p><p>Expanding DAA access was associated with a drop in HCV-related hospitalizations in the overall Canadian population coinciding with the 2014 policy change. In light of the time required for HCV-related complications to manifest, continued ongoing research examining the real-world effectiveness of DAAs is required.</p></div>
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.128 | 0.045 |
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; both teacher heads agree on what is shown here.
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".