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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".