Relation Between Hepatitis C Virus Exposure and Risk of Osteoporosis
Bibliographic record
Abstract
The effect of hepatitis C virus (HCV) exposure on bone mineral density without advanced liver disease remains debated. Thus, we assessed the relation between HCV exposure and the risk of osteoporosis.From 2000 to 2011, patients aged >20 years with HCV exposure were identified from the Longitudinal Health Insurance Database 2000. Of the 51,535 sampled patients, 41,228 and 10,307 patients were categorized as the comparison and the HCV exposure cohorts, respectively.The overall incidence of osteoporosis in the HCV exposure cohort was higher than in the comparison cohort (8.27 vs 6.19 per 1000 person-years; crude hazard ratio = 1.33, 95% confidence interval = 1.20-1.47). The incidence of osteoporosis, higher in women than in men, increased with age and comorbidity of hypertension, hyperlipidemia, and heart failure. The risk of developing osteoporosis was significantly higher in the HCV exposure cohort than in the comparison cohort after adjusting for age, sex, diabetes, hypertension, hyperlipidemia, heart failure, stroke, and cirrhosis. However, the risk of osteoporosis contributed by HCV decreased with age and the presence of comorbidity. Furthermore, the risk of osteoporotic fracture did not differ significantly between patients exposed to HCV and the comparison cohorts.HCV increases the risk of osteoporosis, but no detrimental effect on osteoporotic fracture was observed in this study. Furthermore, HCV may be less influential than other risk factors, such as hypertension, hyperlipidemia, and heart failure, in contributing to the development of osteoporosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".