Diagnosis and treatment of hepatitis C virus infection: a tool for engagement with people who inject drugs in Vancouver’s Downtown Eastside
Bibliographic record
Abstract
Background Vancouver’s Downtown Eastside (DTES) faces the interrelated challenges of poverty, homelessness, mental health, addiction, and medical issues such as hepatitis C virus (HCV). This study evaluates a new model of engagement with people who inject drugs (PWID) in the DTES. Methods Our centre has developed the community pop-up clinic (CPC) to engage vulnerable populations such as PWID. Rapid HCV testing is offered using the OraQuick saliva assay. If a test is positive, immediate medical consultation and an incentivized clinic appointment are offered. At this appointment, an HCV treatment plan is developed, along with a plan for engagement in multidisciplinary care. Results In 12 months, 1,283 OraQuick tests were performed at 44 CPCs; 21% of individuals were found to be positive for HCV (68% of whom were PWID). Of individuals positive for HCV antibodies who consulted with the on-site doctor, 50% engaged in care in our clinic—61% of whom have initiated interferon-free directly acting antiviral (DAA) HCV therapy with 100% cured of HCV (per protocol). Individuals who did not engage in care were significantly more likely to be homeless (P < .0001). Conclusion CPCs paired with a multidisciplinary model of care address the needs of vulnerable populations such as PWID, particularly in the management of HCV with interferon-free DAA therapies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".