Increasing Treatment Rates for Hepatitis C in Primary Care
Bibliographic record
Abstract
BACKGROUND: Despite antiviral agents that can cure the disease, many individuals with Hepatitis C Virus (HCV) remain untreated. Primary care clinicians can play an important role in HCV treatment but often feel they do not have the requisite skills. METHODS: We implemented a population-based improvement intervention over 10 months to support treatment of HCV in a primary care setting. The intervention included a decision-support tool, education for clinicians, enhanced interprofessional team supports, mentorship, and proactive patient outreach. We used process and outcome measures to understand the impact on the proportion of patients who initiated treatment and achieved Sustained Virologic Response (SVR). We used physician focus groups and pharmacist interviews to understand the context and mechanisms influencing the impact of the intervention. RESULTS: Between December 2018 and June 2020, the percentage of HCV RNA positive patients who started treatment rose from 66.0% (354/536) to 75.5% (401/531) with 92.5% (371/401) of those starting treatment achieving SVR. Qualitative findings highlighted that the intervention helped raise awareness and confidence among physicians for treating HCV in primary care. A collaborative team environment, education, mentorship, and a decision-support tool integrated into the electronic record were all enablers of success although patient psychosocial complexity remained a barrier to engagement in treatment. CONCLUSION: A multifaceted primary care improvement initiative increased clinician confidence and was associated with an increase in the proportion of HCV RNA positive patients who initiated curative treatment.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".