Printed in U.S.A. Hepatitis C Prevalence and Risk Factors in the Northern Alberta Dialysis Population
Bibliographic record
Abstract
Hepatitis C virus (HCV) is an emerging global public health issue with particular relevance in multiply transfused renal dialysis patients. This cross-sectional study evaluated the prevalence and risk factors for HCV infection among renal dialysis patients in northern Alberta, Canada. Ninety-two percent of eligible patients (n = 336) provided informed consent to participate. Participants were interviewed to gather risk factor information and, using multiple logistic regression analysis with exact inference, a predictive model for HCV infection in this population was developed. The prevalence of HCV infection in the population was 6.5%, and all positive patients had at least one identifiable risk factor. The multivariate analysis showed that the risk of HCV infection was greater for those in the 18-55 years age category (odds ratio (OR) = 4.9, 95 % confidence interval (Cl) 1.2-27.9), patients who had been on dialysis>5 years (OR = 3.7, 95 % Cl 1.2-12.0), and patients who had>2 high risk lifestyle behaviors (OR = 5.0, 95 % Cl 1.5-16.7). Transfusion prior to 1990 was marginally associated with HCV status (OR = 4.0, 95 % Cl 0.96-16.3). This study documented previously unreported life-style risk factors for HCV infection in patients with renal failure, confirmed the expected decline in transfusion-acquired HCV infection in this population, and provided evidence against nosocomial transmission of HCV. Am J Epidemiol 1999; 150:58-66. blood transfusion; cross infection; cross-sectional studies; dialysis; hepatitis C virus; prevalence; risk 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.281 | 0.072 |
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".