Hepatitis B virus elimination status and strategies in circumpolar countries, 2020
Bibliographic record
Abstract
Hepatitis B virus (HBV) infection remains a global health threat. The World Health Organization (WHO) established a goal to eliminate HBV infection as a public health threat by 2030, and defined targets for key interventions to achieve that goal. We evaluated HBV burden and relevant national recommendations for progress towards WHO targets in circumpolar countries. Viral hepatitis experts of circumpolar countries were surveyed regarding their country's burden of HBV, achievement of WHO targets and national public health authority recommendations for HBV prevention and control. Eight of nine circumpolar countries responded. All countries continue to see new HBV infections. Data about HBV prevalence and progress in reaching WHO 2030 elimination targets are lacking. No country was able to report data for all seven WHO target measures. All countries have recommendations targeting the prevention of mother-to-child transmission. Only the USA and Greenland recommend universal birth dose vaccination. Four countries have recommendations to screen persons at high risk for HBV. Existing recommendations largely address prevention; however, recommendations for universal birth dose vaccination have not been widely introduced. Opportunities remain for the development of trackable targets and national elimination planning to screen and treat for HBV to reduce incidence and mortality.
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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 | 0.004 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".