Anti-JC Virus Antibody Prevalence in Canadian MS Patients (P4.141)
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of anti-JC virus (JCV) antibodies in multiple sclerosis (MS) patients from Canada. BACKGROUND: Seroprevalence of anti-JCV antibodies in the general population has been reported over a wide range from 39-91%, depending on assay methodology and population studied. DESIGN/METHODS: This cross-sectional, multicenter epidemiology study consisted of one visit at which patient information (birthdate, gender, race, type and duration of MS, and number and duration of MS therapies) and one blood sample were collected for analysis of anti-JCV antibodies using a two-step enzyme-linked immunosorbent assay (ELISA; STRATIFY JCVTM). Chi-square and logistic regression (univariate and multivariate) analyses were used to evaluate anti-JCV antibody prevalence by demographics, geographic region, disease characteristics and treatment. RESULTS: The study included 4198 Canadian MS patients. Overall anti-JCV antibody prevalence was 56.3% (95% confidence interval [CI]: 54.8-57.8). Seroprevalence was significantly associated with increasing age and male gender (P<0.0001). Seroprevalence by geographic region ranged from 50.6% (Western) to 59.3% (Quebec), with 55.4% in Atlantic and 55.8% in Ontario regions, and was significantly different after adjustment for age, gender, and immunosuppressant use (P=0.0051). Anti-JCV antibody prevalence was not associated with race, MS disease subtype and disease duration, or number and duration of treatments. MS patients with prior use of immunosuppressive therapy had a higher seroprevalence rate (63.4%; 95% CI: 56.6-69.9) compared with those who had not received immunosuppressive therapy (55.9%; 95% CI: 54.4-57.5; P=0.04, adjusted for age, gender, and region). CONCLUSIONS: The overall estimated anti-JCV antibody prevalence rate in Canada was 56.3%. This finding is consistent with previous experience with the assay, which detected positivity in 55% of natalizumab-treated MS patients (Ann Neurol 2010;68:295-303). We found significant associations of anti-JCV antibody positivity with region, age, gender and prior immunosuppressive therapy, whereas seroprevalence was not associated with race, MS type, duration of MS or number or duration of MS treatments. Study Supported by: Biogen Idec.
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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 | 0.001 | 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".