Anti-JC Virus Antibody Prevalence in Canadian MS Patients
Bibliographic record
Abstract
BACKGROUND: Anti-John Cunningham (JCV) antibodies have been detected in approximately 50% to 60% of multiple sclerosis (MS) patients. Age, sex, and geographic location have been associated with seroprevalence differences. We describe anti-JCV antibody prevalence in the Canadian cohort of patients enrolled in the JCV Epidemiology in MS study. METHODS: This cross-sectional multicenter study evaluated the effects of demographic and disease characteristics on anti-JCV antibody seroprevalence in MS patients irrespective of disease type and treatment. A single blood sample was collected for analysis of anti-JCV antibodies using a two-step enzyme-linked immunosorbent assay (ELISA). Chi-square and logistic regression tests were used to determine significance. RESULTS: A total of 4198 Canadian MS patients participated in the study; the overall anti-JCV antibody prevalence was 56.3% (95% confidence interval: 54.8% to 57.8%). Seroprevalence was significantly associated with age (increasing from 45% in young to 61% in those >60 years), sex, and region (p<0.0001 for age and sex; p=0.005 for region). No significant differences in anti-JCV antibody prevalence were associated with race, MS disease type and duration, or number and duration of treatments. Immunosuppressant use was associated with a higher seroprevalence rate (63.4%) compared with no immunosuppressant use (55.9%; p=0.040). CONCLUSIONS: Canadian MS patients had an overall anti-JCV antibody seroprevalence that was consistent with previous studies using the two-step ELISA. Significant associations of anti-JCV antibody positivity were found with age, sex, region, and immunosuppressant therapy, whereas seroprevalence was not associated with race, MS type, MS duration, or number or duration of MS treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".