Serum hepatitis B virus RNA level is associated with biochemical relapse in patients with chronic hepatitis B infection who discontinue nucleos(t)ide analogue treatment
Bibliographic record
Abstract
BACKGROUND: Nucleos(t)ide analogue (NA) discontinuation may be attempted in carefully selected patients with chronic hepatitis B (CHB) infection. AIM: To investigate whether a novel serum marker of quantitative hepatitis B virus (HBV) RNA levels could predict biochemical relapse after NA discontinuation. METHODS: We prospepctively followed non-cirrhotic Asian patients with CHB who stopped NA according to pre-specified stopping criteria. The primary endpoint was biochemical relapse (HBV DNA >2000 IU/mL and alanine transaminase >2x upper limit of normal), which were also the re-treatment criteria. RESULTS: Biochemical relapse occurred in 50 patients (48.3% at year 6). Multivariable analysis showed that higher HBV RNA levels (HR 1.34; P < 0.001) at the time of NA discontinuation were associated with increased biochemical relapse risk. The area under the curve of HBV RNA at the time of NA discontinuation for the incidence of biochemical relapse was 0.760 at 6 years. Six years after treatment discontinuation, all patients with HBV RNA levels ≥20 000 copies/mL at the end of treatment developed a biochemical relapse compared with 23.8% of patients with HBV RNA levels<1000 copies/mL (P < 0.001). More patients with HBV RNA levels <1000 copies/mL at end of treatment achieved loss of hepatitis B surface antigen than patients with higher levels (30.9% vs 1.6%; P = 0.027). CONCLUSIONS: The HBV RNA level at end of treatment predicted biochemical relapse after treatment discontinuation and may be used to guide decisions on treatment discontinuation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".