Immigration and viral hepatitis
Bibliographic record
Abstract
WHO estimates reveal that the global prevalence of viral hepatitis may be as high as 500 million, with an annual mortality rate of up to 1.3 million individuals. The majority of this global burden of disease is borne by nations of the developing world with high rates of vertical and iatrogenic transmission of HBV and HCV, as well as poor access to healthcare. In 2013, 3.2% of the global population (231 million individuals) migrated into a new host nation. Migrants predominantly originate from the developing countries of the south, into the developed economies of North America and Western Europe. This mass migration of individuals from areas of high-prevalence of viral hepatitis poses a unique challenge to the healthcare systems of the host nations. Due to a lack of universal standards for screening, vaccination and treatment of viral hepatitis, the burden of chronic liver disease and hepatocellular carcinoma continues to increase among migrant populations globally. Efforts to increase case identification and treatment among migrants have largely been limited to small outreach programs in urban centers, such that the majority of migrants with viral hepatitis continue to remain unaware of their infection. This review summarizes the data on prevalence of viral hepatitis and burden of chronic liver disease among migrants, current standards for screening and treatment of immigrants and refugees, and efforts to improve the identification and treatment of viral hepatitis among migrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".