Bibliographic record
Abstract
The natural history of individuals chronically infected with hepatitis B typically fluctuates, with periods of active viral replication with or without an associated hepatitis and sometimes prolonged periods of spontaneous viral suppression and inactive liver disease. In the majority, this chronic infection is clinically silent unless either liver failure and/or hepatocellular carcinoma (HCC) supervenes. Thus proactive steps are needed to first identify those with hepatitis B infection and to then serially monitor those found to be chronically infected for both level of alanine aminotransferase (ALT) and hepatitis B virus DNA (HBV-DNA) (using sensitive polymerase chain reaction techniques. Antiviral therapy significantly reduces the risk of liver disease progression and HCC in those with ongoing viral replication > 10(5) c/mL and advanced hepatic fibrosis. The decision of when to initiate (possibly lifelong) treatment has to be made judiciously. Before introducing therapy both patient and physician must recognise the need for compliance with both treatment and viral surveillance so as to minimise the development of drug resistance. Drug resistance needs to be identified prior to recurrence of hepatitis (rise in ALT) to prevent hepatic decompensation, this necessitates serial HBV-DNA testing.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".