Acute hepatitis C infection among adults with HIV in the Netherlands: a capture-recapture analysis
Bibliographic record
Abstract
Abstract Background Reliable surveillance systems are essential to assess the national response to eliminating hepatitis C virus (HCV), in the context of the global strategy towards eliminating viral hepatitis. Aim We aimed to assess the completeness of the two national registries of acute HCV infection in people with HIV, and estimated the number of acute HCV infections among adults with HIV in the Netherlands. Methods For 2003-2016, cases of HCV infection and reinfection among adults with a positive or unknown HIV-serostatus were identified in two national registries: the ATHENA cohort, and the National Registry for Notifiable Diseases. For 2013-2016, cases were linked, and two-way capture-recapture analysis was carried out. Results During 2013-2016, there were an estimated 282 (95%CI: 264-301) acute HCV infections among adults with HIV. The addition of cases with an unknown HIV-serostatus increased the matches (from N=104 to N=129), and a subsequently increased the estimated total: 330 (95%CI: 309-351). Underreporting was estimated at 14-20%. Conclusion In 2013-2016, up to 330 cases of acute HCV infection were estimated to have occurred among adults with HIV. National surveillance of acute HCV can be improved by increased notification of infections. Surveillance data should ideally include both acute and chronic HCV infections, and be able to distinguish between acute and chronic infections, and initial and reinfections. Classifications The Netherlands; sexually transmitted infections; hepatitis C; HIV infection; Surveillance; epidemiology
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".