Incidence and prediction of HBsAg seroclearance in a prospective multi‐ethnic HBeAg‐negative chronic hepatitis B cohort
Bibliographic record
Abstract
BACKGROUND AND AIMS: Achieving HBsAg loss is an important landmark in the natural history of chronic hepatitis B (CHB). A more personalized approach to prediction of HBsAg loss is relevant in counseling patients. This study sought to develop and validate a prediction model for HBsAg loss based on quantitative HBsAg levels (qHBsAg) and other baseline characteristics. METHODS: The Hepatitis B Research Network (HBRN) is a prospective cohort including 1240 untreated HBeAg-negative patients (1150 adults, 90 children) with median follow-up of 5.5 years. Incidence rates of HBsAg loss and hepatitis B surface antibody (anti-HBs) acquisition were determined, and a predictor score of HBsAg loss using readily available variables was developed and externally validated. RESULTS: Crude incidence rates of HBsAg loss and anti-HBs acquisition were 1.6 and 1.1 per 100 person-years (PY); 67 achieved sustained HBsAg loss for an incidence rate of 1.2 per 100 PY. Increased HBsAg loss was significantly associated with older age, non-Asian race, HBV phenotype (inactive CHB vs. others), HBV genotype A, lower HBV-DNA levels, and lower and greater change in qHBsAg. The HBRN-SQuARe (sex,∆quantHBsAg, age, race) score predicted HBsAg loss over time with area under the receiver operating characteristic curve (AUROC) (95% CIs) at 1 and 3 years of 0.99 (95% CI: 0.987-1.00) and 0.95 (95% CI 0.91-1.00), respectively. In validation in another cohort of 1253 HBeAg-negative patients with median follow-up of 3.1 years, HBRN SQuARe predicted HBsAg loss at 1 and 3 years with AUROC values of 0.99 (0.98-1.00) and 0.88 (0.77-0.99), respectively. CONCLUSION: HBsAg loss in predominantly untreated patients with HBeAg-negative CHB can be accurately predicted over a 3-year horizon using a simple validated score (HBRN SQuARe). This prognostication tool can be used to support patient care and counseling.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".