Decentralized, Community-Based Hepatitis C Point-of-Care Testing and Direct-Acting Antiviral Treatment for People Who Inject Drugs and the General Population in Myanmar: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The advent of direct-acting antivirals (DAAs) and point-of-care (POC) testing platforms for hepatitis C allow for the decentralization of care to primary care settings. In many countries, access to DAAs is generally limited to tertiary hospitals, with limited published research documenting decentralized models of care in low-and middle-income settings. OBJECTIVE: This study aims to assess the feasibility, acceptability, effectiveness, and cost-effectiveness of decentralized community-based POC testing and DAA therapy for hepatitis C among people who inject drugs and the general population in Yangon, Myanmar. METHODS: Rapid diagnostic tests for anti-hepatitis C antibodies were carried out on-site and, if reactive, were followed by POC GeneXpert hepatitis C RNA polymerase chain reaction tests. External laboratory blood tests to exclude other major health issues were undertaken. Results were given to participants at their next appointment, with the participants commencing DAA therapy that day if a specialist review was not required. Standard clinical data were collected, and the participants completed behavioral questionnaires. The primary outcome measures are the proportion of participants receiving GeneXpert hepatitis C RNA test, the proportion of participants commencing DAA therapy, the proportion of participants completing DAA therapy, and the proportion of participants achieving sustained virological response 12 weeks after completing DAA therapy. RESULTS: Recruitment was completed on September 30, 2019. Monitoring visits and treatment outcome visits are scheduled to continue until June 2020. CONCLUSIONS: This feasibility study in Myanmar contributes to the evidence gap for community-based hepatitis C care in low- and middle-income settings. Evidence from this study will inform the scale-up of hepatitis C treatment programs in Myanmar and globally.
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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 | 0.046 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
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".