Factors associated with specialist assessment and treatment for hepatitis C virus infection in New South Wales, Australia
Bibliographic record
Abstract
Assessment and treatment for hepatitis C virus (HCV) in the community remains low. We evaluated factors associated with HCV specialist assessment and treatment in a cross-sectional study to evaluate treatment considerations in a sample of 634 participants with self-reported HCV infection in New South Wales, Australia. Participants having received HCV specialist assessment (n = 294, 46%) were more likely to be have been older (vs <35 years; 35-44 OR 1.64, P = 0.117; 45-54 OR 2.00, P = 0.024; ≥55 OR 5.43, P = 0.002), have greater social support (vs low; medium OR 3.07, P = 0.004; high OR 4.31, P < 0.001), HCV-related/attributed symptoms (vs none; 1-10 OR 3.89, P = 0.032; 10-21 OR 5.01, P = 0.010), a diagnosis of cirrhosis (OR 2.40, P = 0.030), have asked for treatment information (OR 1.91, P = 0.020), have greater HCV knowledge (OR 2.49, P = 0.001), have been told by a doctor to go onto treatment (OR 3.00, P < 0.001), and less likely to be receiving opiate substitution therapy (OR 0.10, P < 0.001) and never to have seen a general practitioner (OR 0.24, P < 0.001). Participants having received HCV treatment (n = 154, 24%) were more likely to have greater fibrosis (vs no biopsy; none/minimal OR 3.45, P = 0.001; moderate OR 11.47, P < 0.001; severe, OR 19.51, P < 0.001), greater HCV knowledge (OR 2.57; P = 0.004), know someone who has died from HCV (OR 2.57, P = 0.004), been told by a doctor to go onto treatment (OR 3.49, P < 0.001), were less likely to have been female (OR 0.39, P = 0.002), have recently injected (OR 0.42, P = 0.002) and be receiving opiate substitution therapy (OR 0.22, P < 0.001). These data identify modifiable patient-, provider- and systems-level barriers associated with HCV assessment and treatment in the community that could be addressed by targeted interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".