Hepatitis B vaccination in countries with low endemicity.
Bibliographic record
Abstract
(1) Hepatitis B is less frequent in rich countries than in the rest of the world. The prevalence of HBs antigen (a measure of the risk of viral transmission) ranges from less than 0.1% in northern Europe to 1-5% in southern Europe and 8-20% in many African and Asian countries. (2) Initial recommendations to vaccinate only those in high risk groups had no apparent impact on the hepatitis B transmission rate. Changes in behaviour linked to the AIDS pandemic have coincided with a fall in the incidence of acute hepatitis B. (3) The main limitation of immunisation strategies restricted to high risk groups is the fact that no known risk factor is identified in over 30% of cases of acute hepatitis B. (4) All rich countries have recommended routine screening for hepatitis B in pregnant women, and immunisation of newborns whose mothers carry HBs antigen. (5) In countries with low endemicity, HBV transmission usually occurs after the age of 20. Universal vaccination of infants would, therefore, have no effect on the HBV transmission rate for 15-20 years, and then only if the coverage was extensive. Countries in northern Europe, where the prevalence of HBV infection is the lowest in the world, together with Canada, Switzerland and Australia, have not recommended universal immunisation of infants. (6) Universal immunisation of adolescents covers a population closer to the ages most at risk. Only northern European countries have not adopted this policy.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".