Concordance between self‐reported and current hepatitis C virus infection status in a sample of people who inject drugs in Sydney and Canberra, Australia
Bibliographic record
Abstract
INTRODUCTION: Awareness of hepatitis C virus (HCV) status among people who inject drugs is critical to ensure linkage to care and reduce transmission risk. Testing pathways, confusion about results and possible reinfection raise potential for discordance between perceived and actual HCV status among people who inject drugs. We evaluated self-reported and serologically confirmed HCV status concordance among a sample of Australian people who inject drugs. METHODS: Data were collected in May-June 2018 from participants in Canberra and Sydney, Australia, who had injected drugs at least monthly in the past 6 months. Participants completed a structured interview assessing self-reported HCV status and provided a dried blood spot sample for HCV RNA testing. RESULTS: Of 103 participants, 95% self-reported ever receiving antibody testing, 58% of whom reported having received RNA testing. Seventy-three percent of participants reported never having been told that they had HCV, 18% reported current infection and 9% did not know their current status. According to dried blood spot RNA testing, 20% were currently infected. Over a quarter of the sample (28%, n = 29) did not accurately report their HCV status, half of whom were unaware of a current infection. DISCUSSION AND CONCLUSIONS: With over one-quarter of the sample in our study not accurately reporting their current HCV status, our findings reinforce the importance of regular testing for active infection, and the need for improved health literacy on HCV antibody and RNA test results, HCV status post-treatment and reinfection risk.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".