Influence of female sex on hepatitis C virus infection progression and treatment outcomes
Bibliographic record
Abstract
BACKGROUND: The influence of sex on hepatitis C virus (HCV)-related outcomes is often neglected. The effects of sex on liver fibrosis progression and the effect of socioeconomic status on management are unclear. PATIENTS AND METHODS: Data were evaluated from patients followed at The Ottawa Hospital and Regional Viral Hepatitis Program. RESULTS: Of 1978 chronic HCV-infected patients, 630 (32%) were women. Women had lower liver enzyme levels, HCV RNA levels, and weight compared with men. Women were more likely to be non-genotype-1 infected, Black or Asian, and immigrants from Africa and Asia (all P<0.01). Under 50 years of age, women on average had lower fibrosis scores than men. Beyond the age of 50 years, the mean fibrosis scores were similar, suggesting a 'catch-up' phase. Women were less likely to have initiated interferon-based HCV antiviral therapy (35.3 vs. 43.3%, P=0.01). Crude sustained virological responses were higher in women (65.3 vs. 56.3%, P=0.03), but were similar to men as determined by multivariable analysis (odds ratio: 0.92, 95% confidence interval: 0.58-1.46). Women of low socioeconomic status were more likely to be HIV coinfected and had higher rates of fibrosis progression. Women living in low-income neighborhoods were less likely to achieve sustained virological response (odds ratio: 0.50, 95% confidence interval: 0.34-0.75, P=0.01) compared with women in higher income regions. CONCLUSION: Sex differences have been identified as a potential barrier to overcome when managing viral infections. Our analysis suggests that sex influences fibrosis progression, likelihood of initiating HCV antiviral therapy, and treatment outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".