Transitioning to Highly Effective Therapies for the Treatment of Chronic Hepatitis C Virus Infection: A Policy Statement and Implementation Guideline
Bibliographic record
Abstract
Chronic hepatitis C virus (HCV) infection increases all-cause mortality, rates of cirrhosis, hepatocellular carcinoma, liver transplantation and overall health care utilization. Morbidity and mortality disproportionately affect individuals born between 1945 and 1975. The recent development of well-tolerated and highly effective therapies for chronic HCV infection represents a unique opportunity to dramatically reduce rates of HCV-related complications and their costs. Critical to the introduction of such therapies will be well-designed provincial programming to ensure immediate treatment access to individuals at highest risk for complication, and well-defined strategies to address the global treatment needs of traditionally high-risk and marginalized populations. HCV practitioners in New Brunswick created a provincial strategy that stratifies treatment according to those at highest need, measures clinical impact, and creates evaluation strategies to demonstrate the significant direct and indirect cost savings anticipated with curative treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".