Bibliographic record
Abstract
Chronic Hepatitis B is a common problem, especially in Asian countries. This disease causes complications of cirrhosis and liver cancer. Therefore doctors and patients are concerned whether to treat or screen for these complications. We searched the literature for evidence to determine the risk for people with chronic hepatitis B, the evidence that treating patients changes their outcome, and the effect of screening on death rates. We found little evidence from high quality cohort studies to demonstrate the outcome of chronic hepatitis B infection. Consequently, we constructed a mathematical model to demonstrate outcome for them. The model showed that as a result of having chronic hepatitis B, men lose a mean of 7 years of life, whereas women lose only 2 years. While antiviral treatments change the serological status and reduce liver inflammation, there is insufficient information about their effect on cancer reduction. Our Cochrane review of screening for liver cancer in chronic infection shows no high quality randomised controlled trials and poor non-trial evidence. It appears unlikely that screening programs are effective in reducing mortality for this disease, a conclusion shared by other groups. Therefore, at present, doctors are limited in what we can do to change the outcome for this group of patients.
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 | 0.013 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".