Use of HBV RNA and to predict change in serological status and disease activity in CHB
Bibliographic record
Abstract
BACKGROUND AND AIMS: Predicting changes in disease activity and serological endpoints is necessary for the management of patients with chronic hepatitis B (CHB). We examined whether HBV RNA and hepatitis B core-related antigen (HBcrAg), two specialized virological markers proposed to reflect the activity of covalently closed circular DNA, may improve the ability to predict not sustained inactive carrier phase, spontaneous alanine aminotransferase (ALT) flare, HBeAg loss, and HBsAg loss. APPROACH AND RESULTS: Among eligible participants enrolled in the North American Hepatitis B Research Network Adult Cohort Study, we evaluated demographic, clinical, and virologic characteristics, including HBV RNA and HBcrAg, to predict not sustained inactive carrier phase, ALT flare, HBeAg loss, and HBsAg loss through a series of Cox proportional hazard or logistic regression models, controlling for antiviral therapy use. Among the study population, 54/103 participants experienced not sustained inactive carrier phase, 41/1006 had a spontaneous ALT flare, 83/250 lost HBeAg, and 54/1127 lost HBsAg. HBV RNA or HBcrAg were predictive of all 4 events. However, their addition to models of the readily available host (age, sex, race/ethnicity), clinical (ALT, use of antiviral therapy), and viral factors (HBV DNA), which had acceptable-excellent accuracy (e.g., AUC = 0.72 for ALT flare, 0.92 for HBeAg loss, and 0.91 for HBsAg loss), provided only small improvements in predictive ability. CONCLUSION: Given the high predictive ability of readily available markers, HBcrAg and HBV RNA have a limited role in improving the prediction of key serologic and clinical events in patients with CHB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".