Towards elimination of hepatitis B and C in European Union and European Economic Area countries: monitoring the World Health Organization’s global health sector strategy core indicators and scaling up key interventions
Bibliographic record
Abstract
The World Health Organization 'Global Health Sector Strategy on Viral Hepatitis 2016-2021' aimed at the elimination of viral hepatitis as a public health threat provides a significant opportunity to increase efforts for tackling the epidemics of hepatitis B and hepatitis C virus infections across Europe. To support the implementation and monitoring of this strategy, core epidemiological and programmatic indicators have been proposed necessitating specific surveys, the systematic collection of programmatic data and the establishment of monitoring across the care pathway. European Union and European Economic Area countries already made progress in recent years implementing primary and secondary prevention measures. Indeed, harm reduction measures among people who inject drugs reach many of those who need them and most countries have a universal hepatitis B vaccination programme with high coverage above 95%. However, while a further scaling up of prevention interventions will impact on incidence of new infections, treating those already infected is necessary to achieve reductions in mortality. The epidemiological, demographic and socio-political situation in Europe is complex, and considerable diversity in the programmatic responses to the hepatitis epidemic exists. Comprehension of such issues alongside collaboration between key organisations and countries will underpin any chance of successfully eliminating hepatitis.
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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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".