Hepatitis B and Hepatitis C Viral Infections in Patients with Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
BACKGROUND: Whether chronic hepatitis B virus (HBV) or hepatitis C virus (HCV) infections contribute to the pathogenesis and⁄or course of chronic lymphocytic leukemia is unclear. OBJECTIVE: To document the prevalences of HBV and HCV infections in chronic lymphocytic leukemia patients, and to determine whether infected patients experience more aggressive disease than those without infection. METHODS: Patient sera were screened for antibodies to HBV core antigen and HCV (anti-HCV) using ELISA; both sera and peripheral blood lymphocytes were further tested (regardless of antibody results) for HBV-DNA and HCV-RNA using real-time polymerase chain reaction. Prognostic markers for chronic lymphocytic leukemia included Rai stage, IgVH mutational status, β2-microglobulin levels, Zap-70 and CD38 status. RESULTS: Fourteen of 222 (6.3%) chronic lymphocytic leukemia patients and two of 72 (2.8%) healthy controls tested positive for previous or ongoing HBV infection (OR 2.4 [95% CI 0.5 to 7.7]; P=0.25) while four of 222 (1.8%) chronic lymphocytic leukemia patients and one of 72 (1.4%) controls tested positive for HCV markers (OR 1.3 [95% CI 0.2 to 6.4]; P=0.81). The levels and distribution of the various indicators of aggressive chronic lymphocytic leukemia disease were similar among HBV- and HCV-infected and uninfected patients. Survival times were also similar. Occult HBV and HCV infection (HBV-DNA or HCV-RNA positive in the absence of diagnostic serological markers) were uncommon in chronic lymphocytic leukemia patients (0.5% and 1.8%, respectively). CONCLUSIONS: The results of the present study do not support the hypothesis that HBV or HCV infections play an important role in the pathogenesis or course of chronic lymphocytic leukemia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".