Association of hepatitis C infection and risk of kidney cancer: A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
Abstract Although some epidemiological studies have investigated the association between Hepatitis C virus (HCV) infection and the development of kidney cancer, the results are far from consistent. We conducted a systematic review and meta‐analysis of observational studies to determine the association. PubMed, EMBASE and Cochrane database were searched from 1 January 1975 to 7 January 2020. Study selection, data extraction and bias assessment (using the Newcastle‐Ottawa scale) were performed independently by 2 authors. Pooled odds ratios (ORs) with corresponding confidence intervals (CIs) were calculated using a random‐effects model. In all, 16 studies (11 cohort studies and 5 case‐control studies) involving a total of 391,071 HCV patients and 38,333,839 non‐HCV controls were included. The overall analysis showed a 47% higher risk to develop kidney cancer among the patients with HCV infection (pooled OR 1.47; 95% CI 1.14‐1.91), despite significant heterogeneity ( I 2 = 87.6%). The multivariable meta‐regression showed that study design, age, sample size and HIV co‐infection were significant sources of variance, and totally accounted for 82% of the I 2 . The risk of KC in HCV patients was further increased in studies without HCV/HBV‐ and HCV/HIV‐ co‐infection (pooled OR 1.66; 95%CI 1.23‐2.24). Multiple sensitivity analyses did not change the significant association. The present meta‐analysis indicated that HCV‐infected patients have a significantly higher risk of developing kidney cancer. Our results highlighted the rationale for improved renal surveillance in HCV patients for the early diagnosis of kidney cancer. Further investigations for the mechanisms underlying HCV‐induced kidney cancer are warranted.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".