Bibliographic record
Abstract
Alanine aminotransferase (ALT) level alone is not an appropriate indication for therapy in chronic hepatitis B infection, and other criteria in addition to ALT must be used to determine eligibility for therapy Predictors of survival in chronic hepatitis B infection are surprisingly not well described. Various studies have identified different factors that were associated with adverse outcomes. For example, Niederau and colleagues,1 in a cohort of European patients, identified lack of clearance of hepatitis B e antigen (HBeAg) as a predictor of decreased survival. Others have identified older age, presence of cirrhosis, and the persistence of alanine aminotransferase (ALT) elevations as adverse prognostic signs in an antibody to hepatitis B e antigen (anti-HBe) positive cohort.2 In patients undergoing a flare of hepatitis B activity, whether spontaneous or chemotherapy induced, the presence of jaundice is an ominous sign.3 None of these adverse predictive factors are unexpected. Clearly, jaundice, cirrhosis, older age, and elevated ALT are obvious adverse predictive factors but until recently we have not had the tools to predict, years in advance, the outcome of chronic hepatitis B infection. This is important because we would prefer to offer treatment only to those who are likely to develop complications of the disease, and not to those whose disease will become inactive without long term sequelae. Recently, new predictors of outcome have been identified. At last year’s American Association for Study of Liver Disease (AASLD) meeting and the recent European Association for Study of the Liver (EASL) meeting, new data were presented that contribute to this debate. In addition, the article by Yuen and colleagues4 in this issue of Gut also forces us to re-examine some of our assumptions about hepatitis B prognosis and therefore treatment (see page 1610) . To some extent our current management algorithms …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".