Real-world tertiary referral centre experience stopping nucleos(t)ide analogue therapy in patients with chronic hepatitis B
Bibliographic record
Abstract
BACKGROUND: Identifying strategies for stopping nucleos(t)ide analogues (NUC) in patients with chronic hepatitis B (CHB) is a major goal in CHB management. Our study describes our tertiary-centre experience stopping nucleos(t)ide analogues (NUC) in CHB. METHODS: We conducted a retrospective cohort study of all individuals with CHB seen at the Calgary Liver Unit between January 2009 and May 2020 who stopped NUC. We collected baseline demographics and HBV lab parameters before and after stopping NUC with results stratified by off-treatment durability. Clinical flare was defined as alanine aminotransferase (ALT) above twice upper limit of normal and virological flare as HBV DNA >2000 IU/mL. RESULTS: Forty-seven (3.5%) of the 1337 individuals with CHB stopped NUC therapy. During follow-up, six patients (12.8%) restarted NUCs due to flare. All flares occurred within six months of discontinuation. Median time to restart treatment was 90 days (Q1 65, Q3 133). Upon restarting, all showed suppression of HBV DNA and ALT normalization. Factors associated with restarting NUC therapy included hepatitis B e antigen (HBeAg) positive status at first appointment and longer NUC consolidation therapy. Age, sex, ethnicity, liver stiffness measurement, choice of NUC, and quantitative hepatitis B surface antigen (qHBsAg) level at stopping were not associated with sustained response off-treatment. Six patients had functional cure with HBsAg loss. CONCLUSIONS: Stopping long-term NUC is feasible in HBeAg negative CHB. Hepatic flares can occur despite low levels of qHBsAg. Finite NUC therapy can be considered in eligible patients who are adherent to close monitoring and follow-up, particularly in the first six months after stopping NUC therapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".