GBV‐C/hepatitis G virus infection and non‐Hodgkin lymphoma: a case control study
Bibliographic record
Abstract
We investigated whether there was an association between GBV-C viremia and the development of non-Hodgkin lymphoma (NHL) in 553 NHL cases and 438 controls from British Columbia, Canada. Cases were aged 20-79, diagnosed between March 2000 and February 2004, and resident in Greater Vancouver or Victoria. Cases and controls were tested for GBV-C RNA by RT-PCR and positive samples were genotyped. Overall, GBV-C RNA was detected in 4.5% of NHL cases vs. 1.8% of controls [adjusted odds ratio (OR) = 2.72, 95% confidence interval (CI) = 1.22-6.69]. The association between GBV-C RNA detection and NHL remained even after individuals with a history of prior transfusion, injection drug use and hepatitis C virus sero-positivity were excluded. GBV-C viremia showed the strongest association with diffuse large B cell lymphoma (adjusted OR = 5.18, 95% CI = 2.06-13.71). Genotyping was performed on 29/33 GBV-C RNA positive individuals; genotypes 2a (n = 22); 2b (n = 5) and 3 (n = 2) were identified, consistent with the distribution of genotypes found in North America. This is the largest case-control study to date associating GBV-C viremia and NHL risk. As GBV-C is known to be transmitted through blood products this may have important implications for blood safety.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".