Bibliographic record
Abstract
Since the introduction of the hepatitis B vaccine and other preventive measures, the worldwide prevalence of hepatitis B virus (HBV) infection has fallen. However, chronic infection remains a major global health problem, with more than 350 million people chronically infected and at risk of hepatic decompensation, cirrhosis, and hepatocellular carcinoma. In developed countries like Canada, the burden of disease is greatest among marginalized populations and immigrants from regions where HBV is endemic, making chronic hepatitis B an important but clinically silent public health issue. The approval of the first potent oral antiviral agent in 1998 has revolutionized hepatitis B treatment, and current treatments such as conventional and pegylated interferon alfa and nucleoside and nucleotide analogues are widely used to suppress virus replication, reduces hepatitis activity, and halt disease progression. However, access to the most effective medicines in Canada remains difficult due to lack of coverage from provincial health plans unless evidence of liver cirrhosis is present. As the immigrant population increases in Canada, opportunities for education, screening, prevention and treatment of hepatitis B should be identified to increase awareness and help limit the spread of HBV. This will improve the outcome of HBV infection and reduce the burden on Canada’s health care system.
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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".