Efficacy of TDF, TAF, TMF, and TDF-to-TAF switch in chronic hepatitis B: a network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We compared the efficacy of four treatment strategies for chronic hepatitis B (CHB): tenofovir disoproxil fumarate (TDF), tenofovir alafenamide (TAF), tenofovir amibufenamide (TMF), and TDF-to-TAF switch strategies strategy. We conducted a network meta-analysis to provide evidence-based clinical guidance. METHODS: We systematically retrieved PubMed, Web of Science, Cochrane Library, Embase, and China National Knowledge Infrastructure (CNKI) databases up to April 2025 for randomized controlled trials (RCTs) and cohort studies. We assessed literature quality using the Cochrane Risk of Bias (ROB) tool and Newcastle-Ottawa Scale (NOS). Stata 17 was employed for network meta-analysis, focusing on virological response rate, hepatitis B surface antigen (HBsAg) clearance rate, hepatitis B e antigen (HBeAg) clearance rate, and alanine aminotransferase (ALT) normalization rate. RESULTS: Virological response did not differ between TMF monotherapy and TDF-to-TAF switch (OR = -0.03; 95% CI -0.42 to 0.36). HBsAg clearance was similar between TAF and TMF (95% CI: -1.17-1.14). For the rate of HBeAg loss, both TAF and TMF exhibited favorable therapeutic effects. For ALT normalization rate, TMF monotherapy was the most effective, with an average effect size of 0.11 (95% CI: -0.14-0.37), while TDF monotherapy was generally inferior to the other three treatment strategies. CONCLUSION: Our findings may help guide the selection of individualized CHB treatment strategies. However, given the limited evidence and potential bias, these results are preliminary and must be interpreted with caution. For regimens such as TMF that lack robust data, well-established alternatives should be preferred. Long-term, multi-endpoint studies are required to confirm both efficacy and safety before routine clinical adoption.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".