Bibliographic record
Abstract
There are two new nucleoside analogues available for the management of chronic hepatitis B, adefovir and entecavir, and several more in development. In addition, pegylated interferon has become available. Large-scale population studies have re-emphasized the significance of viral load in predicting a poor outcome over the longer term. These new developments have prompted a reassessment of the indications and objectives of therapy for chronic hepatitis B. Hepatitis B virus deoxyribonucleic acid, rather than alanine aminotransferase should be the prime indication for therapy. Hepatitis B e antigen seroconversion can be achieved in 30-40% of treated patients whatever agent is used. However, it takes longer for nucleoside analogues to achieve the same seroconversion rates as interferon. In anti-HBe-positive disease long-term therapy is required for most patients because the relapse rate after withdrawal of therapy is very high, irrespective of the agent used. Viral resistance limits the use of lamivudine, and to a lesser extent adefovir. Resistance to entecavir has so far only been described in pre-existing lamivudine resistance. Although therapy with combinations of nucleoside analogues has not been investigated to any extent, this is the only way to reduce the emergence or resistance, and studies are urgently needed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.124 | 0.120 |
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".