Efficacy and Safety of Tenofovir Alafenamide (TAF) and Tenofovir Disoproxil Fumarate (TDF) Followed by TAF in Chronic Hepatitis B Patients of East Asian Ethnicity Following 5 Years of Treatment
Bibliographic record
Abstract
BACKGROUND: Tenofovir alafenamide (TAF) has shown non-inferior efficacy to tenofovir disoproxil fumarate (TDF), with superior bone and renal safety. AIM: To characterise 5-year TAF efficacy and safety in patients of East Asian ethnicity from pivotal Phase 3 studies. METHODS: Patients were randomised (2:1) to receive TAF or TDF for up to 3 years of double-blind treatment, followed by open-label TAF. Patients either continued TAF or switched from TDF to TAF at Week 96 (TDF → TAF 3 years) or Week 144 (TDF → TAF 2 years) of treatment. Efficacy endpoints (virologic, biochemical and serologic) and safety were assessed. RESULTS: Among 591 patients of East Asian ethnicity (TAF, n = 401; TDF → TAF 3 years, n = 84; TDF → TAF 2 years, n = 106), high rates of virologic control were achieved (89%, 94% and 92%, respectively) at Year 5 (missing = failure analysis). At Year 5, rates of alanine aminotransferase normalisation (85%, 90% and 78%) and hepatitis B e antigen loss (36%, 43% and 44%) were similar. Following the switch from TDF to TAF, changes in fasting lipid parameters were consistent with removal of the known lipid-lowering effect of TDF. However, changes in the total cholesterol to high-density lipoprotein ratio (marker of cardiovascular risk) were minimal and comparable in all groups by Year 5. Renal and bone parameters improved after switching. CONCLUSIONS: Through 5 years, rates of virologic suppression were high in East Asian patients treated with TAF or switched from TDF to TAF. TAF and TDF were well tolerated, with improved renal and bone safety observed in patients switching from TDF to TAF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".