Incidence of hepatitis <scp>C</scp> virus infection in patients on hemodialysis: A systematic review and meta‐analysis
Bibliographic record
Abstract
Hepatitis C virus infection is a perennial concern for hemodialysis units because the prevalence of hepatitis C is significantly higher there than in the general population. Through a systematic review and meta-analysis, we aim to assess the incidence rate of hepatitis C virus infection in hemodialysis units and explore its potential risk factors. Five electronic databases were used to search articles from 1990 to 2012, including PubMed, Embase, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and Wanfang. A random-effects analysis was used to estimate the overall incidence rate of hepatitis C virus infection. A subgroup analysis and meta-regression analysis were conducted to explore factors associated with heterogeneity between studies. Twenty-two eligible articles were found, including 23 incidence rate estimates. The overall incidence rate of hepatitis C virus infection was 1.47 per 100 patient-years (95% confidence interval [CI] 1.14 to 1.80). In the subgroup analysis, the pooled incidence rate was 4.44 (CI 2.65, 6.23) per 100 patient-years in the developing world and 0.99 (CI 0.66, 1.29) per 100 patient-years in the developed world. [Correction added on 2 November 2012, after first online publication: Pooled incidence rate in the developed world has been changed.] In addition, in hemodialysis units with higher prevalence, the incidence rate of hepatitis C virus infection also tended to be higher. Meta-regression analysis showed that the country's development level and initial HCV prevalence combined could explain 67.91% of the observed heterogeneity. The incidence rate of hepatitis C virus infection among patients on hemodialysis was significantly high. Efforts should be taken to control hepatitis C virus infection in hemodialysis units, especially in developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| 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 teacher head, 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".