Early virologic relapse predicts alanine aminotransferase flares after nucleos(t)ide analogue withdrawal in patients with chronic hepatitis B
Bibliographic record
Abstract
When patients with chronic hepatitis B (CHB) stop nucleos(t)ide analogue (NA) therapy before achieving HBsAg loss, flares often ensue which are challenging to predict early. We determined the incidence, severity, outcome and predictors of flares after NA withdrawal. Forty-five patients enrolled in an RCT were included; 107 patients from an external, prospective cohort were used for validation. Retreatment criteria were pre-defined. Pre- and post-treatment predictors of alanine aminotransferase (ALT) flare (>5× ULN) were evaluated by Cox proportional-hazards regression. Seventy-two weeks after NA withdrawal, 23/45 (51%) patients had developed >5× ULN and 14 (31%) >20× ULN. Median time to develop ALT >5× ULN was 12 weeks after NA withdrawal. Independent predictors of ALT >5× ULN were male sex (HR [95% CI] 3.2 [1.2-8.9]; p = 0.03) and serum HBV DNA (1.2 [1.0-1.8]; p = 0.03) at Week 6 off-therapy. Specifically, week 6 HBV DNA >10,000 IU/ml predicted ALT >5× ULN (3.4 [1.4-8.4]; p = 0.01), which was externally validated. In conclusion, this study on post-treatment flares revealed a high cumulative incidence in CHB. Week 6 HBV DNA >10,000 IU/ml independently predicted flares. The proposed threshold enables prediction of imminent flares in patients who may benefit from closer monitoring and earlier retreatment.
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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 | 0.003 | 0.007 |
| 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".