Prevalence of Hepatitis C Virus Infection in a Surgical Population of Southeast China: A Large-Scale Multicenter Study
Bibliographic record
Abstract
Background. Chronic HCV infection affects 80 million people globally and may progress to advanced liver disease. The present study aims to investigate the present epidemiology of HCV infection in a southeastern Chinese surgical patient cohort. Methods. Blood samples obtained from 78,484 surgical patients from 18 different city and county hospitals were enrolled. The incidence of serum HCV antibody positivity, HCV RNA load, and HCV genotyping, as well as demographics and relevant clinical history, were investigated. Data were stratified using the multistage cluster random sampling method and further analyzed using the SPSS-20 package. Results. HCV antibody positivity was detected in 0.15% of the population (95% confidence interval (CI): 0.12%–0.18%). Genotype 1b (55.74%) was the dominant type. The HCV infection peaked in the age groups of 16–20, 41–50, and 61–65 years, and it was higher in males than in females (0.19% vs. 0.13%, P<0.05 ). The geographical distribution of infection rates differed: 0.19% (95% CI: 0.14%–0.24%), 0.18% (95% CI: 0.13%–0.23%), and 0.06% (95% CI: 0.03–0.09%) in plain areas, islands, and valley regions, respectively. Patients with transfusion history and urban residence were associated with high HCV RNA levels (adjusted odds ratio = 11.24 and 6.20, P<0.05 ). Conclusion. The prevalence of HCV infection in this cohort from southeast China was 0.17%, which is lower than the reported 0.43% infection rate in China in 2006. This result can be (partially) explained by the improvement of blood donor screening and the successful campaign for the use of disposable syringes and needles.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".