Hepatitis C Virus and Risk of Non-Hodgkin Lymphoma: A Population-Based Case-Control Study among Connecticut Women
Bibliographic record
Abstract
OBJECTIVES: Previous epidemiologic studies of hepatitis C virus (HCV) infection and B-cell non-Hodgkin lymphoma (B-NHL) have yielded conflicting results, perhaps due to differences in the classification of B-NHL and the choice of non-population-based control groups that may not reflect the background population prevalence of HCV. To further investigate the link between HCV and NHL, we conducted HCV testing on serum samples of 998 women (464 cases; 534 controls) from a population-based case-control study of women in Connecticut. METHODS: Serum samples were screened for HCV antibodies using an enzyme immunoassay; positive samples were confirmed by additional testing for HCV antibodies and for serum HCV RNA. RESULTS: Approximately 2% (8 of 464) of cases and 1% (5 of 534) of controls tested positive for HCV. The risk of NHL associated with HCV infection appeared to be concentrated among B-cell lymphomas [odds ratio (OR) 2.0; 95% confidence interval (CI) 0.6, 8.2], particularly among follicular lymphomas (OR 4.1, 95% CI 0.8, 19.4). CONCLUSIONS: The primary strength of this study is our use of a population-based study design, although the low prevalence of HCV among women in Connecticut resulted in wide CIs for the estimated association between HCV and B-NHL subtypes. Our study suggests that HCV may be associated with increased risk of development of B-NHL, and that this risk may vary by B-NHL subtype among women. Due to the relatively low prevalence of HCV in our study population and the scarcity of population-based epidemiological research on this subject, our study highlights the need for additional large, population-based studies of the role of HCV in the etiology of B-NHL.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".