Internet‐guided <scp>HCV‐RNA</scp> testing: A promising tool to achieve hepatitis C micro‐elimination among men who have sex with men
Bibliographic record
Abstract
In the Netherlands, hepatitis C virus (HCV) transmission occurs primarily in men who have sex with men (MSM). By early diagnosis and immediate treatment of acute HCV infections, HCV micro-elimination in MSM is within reach. In cooperation with the community affected, we developed an online HCV-RNA home-based self-sampling test service. This service combined online HCV self-risk assessment with the possibility to test anonymously for HCV-RNA. The service was available in the Netherlands from February 2018 till December 2020 and was promoted online on various dating sites and offline by community volunteers. Using website user data, test results and an online post-test user survey, we evaluated the service and user experiences. The website page with information about testing was visited by 3401 unique users, of whom 2250 used the HCV-risk assessment tool, 152 individuals purchased 194 HCV-RNA tests, and 104 tests were used, of which 101 gave a conclusive result. The target population of MSM at risk was successfully reached with 44.1% of users receiving the advice to test. The test service had a satisfactory uptake (6.8%, 152/2250), a very high HCV-RNA positivity rate (10.9%, 11/101) and was considered acceptable and easy to use by most MSM. We demonstrate that an HCV-RNA home-based self-sampling test service is successful in diagnosing HCV infections among MSM. This service could be a valuable addition to existing sexual healthcare services as it may reach men who are otherwise not tested.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".