Hepatitis B virus infection as a risk factor for chronic kidney disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Currently, several studies have observed that chronic hepatitis B virus infection is associated with the pathogenesis of kidney disease. However, the extent of the correlation between hepatitis B virus infection and the chronic kidney disease risk remains controversial. METHODS: In the present study, we searched all eligible literature in seven databases in English and Chinese. The random effects model was used to conduct a meta-analysis. Quality of included studies was assessed using the Newcastle-Ottawa Quality Scale. RESULTS: In this analysis, a total of 31 studies reporting the association between hepatitis B virus infection and chronic kidney disease risk were included. The results showed a significant positive association between hepatitis B virus infection and the risk of chronic kidney disease (pooled OR, 1.20; 95% CI, 1.12-1.29), which means that hepatitis B virus increases the risk of developing chronic kidney disease. CONCLUSION: This study found that hepatitis B virus infection was associated with a significantly increased risk of chronic kidney disease. However, the current study still cannot directly determine this causal relationship. Thus, more comprehensive prospective longitudinal studies are needed in the future to provide further exploration and explanation of the association between hepatitis B virus and the risk of developing chronic kidney disease.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".