Comparison of renal safety of tenofovir and entecavir in patients with chronic hepatitis B: Systematic review with meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recently, the American Association for the Study of Liver Disease suggested no preference between tenofovir (TDF) and entecavir (ETV) regarding potential long-term risks of renal complications. Over the years, renal safety has become a critical concern in nucleos(t)ide analog-treated patients due to the long-term use of these drugs. However, existing studies do not show significant differences in renal dysfunction between these two drugs. Further, there is a paucity of studies comparing the long-term renal effects of TDF and ETV. AIM: To investigate the effects of TDF and ETV on renal function, we performed systematic review and meta-analysis. METHODS: Two investigators independently searched the Cochrane Library, MEDLINE, and Embase databases for randomized controlled trials and nonrandomized studies (NRSs) using the keywords "CHB", "Tenofovir", and "Entecavir", and additional references were obtained from the bibliographies of relevant articles published through December 2017. The quality of each study was assessed using the Newcastle-Ottawa scale and the Grading of Recommendations Assessment, Development and Evaluation criteria. The primary outcome was the change in serum creatinine level in the TDF and ETV groups at baseline, 6 mo, 12 mo and 24 mo. RESULTS: = 67%). CONCLUSION: TDF statistically significantly increased serum creatinine levels and decreased the eGFR in 6-24 mo compared to ETV, with moderate to low quality of evidence. However, the differences are negligible.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".