Hepatitis C virus infection and the risk of Sjögren or sicca syndrome: a meta‐analysis
Bibliographic record
Abstract
Previous studies have suggested an association between hepatitis C virus (HCV) infection and the development of Sjögren's syndrome (SS), also known as sicca syndrome. The main objective of this study was to summarize the existing evidence and quantitatively evaluate the association between hepatitis C virus infection and SS/sicca syndrome by performing a meta-analysis of observational studies. MEDLINE and PubMed (January 1980-August 2013) were searched to identify relevant studies in English. Outcomes were calculated and are reported as odds risk (OR) and 95% CIs based on a random-effects model. Heterogeneity was assessed with I(2) statistics. Quality assessment was performed with the Newcastle-Ottawa scale. Based on meta-analysis of five cross-sectional and five cohort studies, a significant positive relationship between HCV infection and development of SS/sicca syndrome was found, the pooled random effects OR being 3.31 (95% CI, 1.46-7.48; P < 0.001). In subset analyses, the studies that used European diagnostic criteria showed a higher summary OR than did studies that adopted other diagnostic criteria. When the data were stratified by source of controls, significant associations were also observed when healthy people (OR = 9.44; 95% CI = 2.67-33.40; P = 0.204) or subjects with hepatitis B virus infection (OR = 6.57; 95% CI = 1.21-35.57; P = 0.5) were used as controls, but not when the controls were hospital-based (OR = 0.99; 95% CI = 0.61-1.61; P = 0.169). In summary, the findings suggest that HCV infection is associated with SS/sicca syndrome. The observed increased risk in studies in which European diagnostic criteria and healthy controls were used and the decreased risk in studies with hospital-based controls may be attributable to selection bias or other unknown factors.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.054 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".