Hepatitis C virus testing in a clinical <scp>HIV</scp> cohort in Ontario, Canada, 2000 to 2015
Bibliographic record
Abstract
BACKGROUND: HIV-positive individuals may acquire HCV via injection drug use (IDU) and condomless anal sex. HIV care provides opportunities for HCV testing and cure with direct-acting antiviral agents (DAAs). METHODS: We analyzed data from the Ontario HIV Treatment Network Cohort Study. Among those not HCV-positive or diagnosed previously (n = 4586), we used Cox regression to test the rates of ever HCV testing (serological or RNA) in HIV care by DAA era (pre-DAA: 2000-2010; after DAA: 2011-2015) and compared the proportion diagnosed with HCV. We identified correlates of annual proportions of serological testing using Poisson generalized estimating equations. RESULTS: < 0.0001). The proportion diagnosed with HCV declined by 74% pre-DAA to 11% after DAAs. Annual testing increased per calendar year (16% steeper slope after DAA vs pre-DAA) and was more common among men who have sex with men; those more educated (post-secondary vs ≤ high school); and those positive for syphilis or reporting any IDU. Annual testing decreased per decade of age and time since HIV diagnosis. DISCUSSION: Annual HCV testing increased over time with higher testing among those reporting sexual or IDU risk factors, but fell short of clinical guidelines. Targeted interventions to boost testing may be needed to close these gaps and reach WHO 2030 HCV elimination targets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".