Anti‐<scp>JC</scp> virus (<scp>JCV</scp>) antibody prevalence in the <scp>JCV</scp> Epidemiology in <scp>MS</scp> (<scp>JEMS</scp>) trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Progressive multifocal leukoencephalopathy (PML) is caused by reactivation of JC virus (JCV) infection due to combined host and viral factors. Anti-JCV antibodies provide a means to assess JCV exposure and stratify PML risk. The reported seroprevalence of anti-JCV antibodies varies from 39% to 91% depending on assay methodology and population studied. A two-step anti-JCV antibody assay (STRATIFY JCV™; Focus Diagnostics, Cypress, CA, USA) detected anti-JCV antibodies in approximately 55% of multiple sclerosis (MS) patients. This study describes the prevalence of anti-JCV antibodies in a large, multinational MS population. METHODS: This cross-sectional epidemiology study was designed to enroll a minimum of 2000 patients with an MS diagnosis of any type, irrespective of treatment, from Europe, Canada and Australia. Anti-JCV antibody prevalence was determined by STRATIFY JCV; the effects of demographic and disease characteristics were evaluated. RESULTS: A total of 7724 patients from 10 countries participated in the study. Overall anti-JCV antibody prevalence was 57.1% (95% confidence interval 56.0%-58.2%). Seroprevalence was significantly associated with increasing age, gender and country of current residence (P < 0.0001). No significant differences in anti-JCV antibody prevalence were associated with MS disease characteristics, including duration and type of MS and number and duration of MS therapies. CONCLUSIONS: Overall seroprevalence of anti-JCV antibodies in MS patients from Europe, Canada and Australia was consistent with previous studies using the STRATIFY JCV assay. Anti-JCV prevalence differed significantly by age, gender and country, but no geographical pattern was evident. Disease and treatment type were not associated with differences in anti-JCV antibody status.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".