Factors Associated with Spontaneous Clearance of Hepatitis C Virus in Chinese Population
Bibliographic record
Abstract
Hepatitis C virus (HCV) infections spontaneously clear in approximately 15-45% of infected individuals. Factors which influence spontaneous HCV clearance remain to be identified. The purpose of the present study was to identify variables associated with spontaneous HCV clearance in a referred population of Chinese patients. The prevalence of host, viral, and environmental factors known to influence the outcome of HCV infections was compared in 92 HCV spontaneous clearance subjects and 318 HCV persistent infection subjects. Univariate and multivariate analyses were performed to identify those factors associated with spontaneous HCV clearance. In univariate analysis, female gender, a history of icteric hepatitis, serologic evidence of concurrent HBV infection, and rs12979860 CC genotype were positively associated with spontaneous HCV clearance, while alcohol consumption was negatively associated with clearance. In multivariate analysis, female gender, a history of icteric hepatitis, concurrent HBV infection, and rs12979860 CC genotype remained independent variables associated with spontaneous HCV clearance. Spontaneous HCV clearance is more likely to occur in females, subjects with a history of icteric hepatitis, HBV coinfections, and those with the rs12979860 CC genotype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".