Impact of Liver Biopsy on the Decision to Treat Patients with Chronic Hepatitis B Genotype D Virus Infection
Bibliographic record
Abstract
OBJECTIVES: Patients with chronic hepatitis B virus (HBV) may exhibit significant liver pathology despite alanine aminotransferase (ALT) and HBV DNA levels below the cutoff values advised by treatment guidelines. We evaluated candidacy for HBV therapy when baseline histopathological changes are taken into consideration. METHODS: Clinical, biochemical, serological, virological, and histopathological (METAVIR score) data of 117 patients with HBeAg-negative chronic HBV genotype D were collected and analyzed. RESULTS: Significant pathology (≥F2 and/or ≥A2) and fibrosis (≥F2 ± ≥A2) were found in 73 (62.4%) and 59 (50.4%) patients, respectively. Based on HBV DNA (>2,000 IU/ml) and ALT levels >2 × 40 U/l (the standard cutoff value), only 31 (26.5%) patients were candidates for therapy. This increased to 58 (49.6%) patients when the new ALT cutoff values (30 U/l for males, and 19 U/l for females) were applied. Relying on either ≥F2 and/or A ≥2 or ≥F2 ± ≥A2 increases the treatment candidacy to 73 (62.4%) and 59 (50.4%) patients, respectively. Also, when compared with standard ALT cutoff values, applying both new ALT cutoff values with either significant pathology or fibrosis increases treatment candidacy to 28 (23.9%) and 42 (35.9%) patients, respectively. CONCLUSION: Liver pathology is more reliable than ALT and HBV DNA in the decision to treat patients with HBeAg-negative chronic HBV genotype D.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".