The evidence for a role of B cells in multiple sclerosis
Bibliographic record
Abstract
Understanding the pathogenesis of complex immunologic disorders such as multiple sclerosis (MS) is challenging. Abnormalities in many different cell types are observed in the immune system and CNS of patients with MS and the identification of the primary and secondary events is difficult. Recent studies suggest that the model of MS as a disorder mediated only by T cells is overly simplistic and propose an important role for B cells in the propagation of the disease. B-cell activation in the form of oligoclonal bands (OCB) production is the most consistent immunologic finding in patients with MS. Notably, markers of B-cell activation within the CSF of patients with MS predict conversion from clinically isolated syndrome to clinically definite MS and correlate with MRI activity, onset of relapses, and disability progression. In addition, the main genetic risk factor in MS is associated with OCB production, and environmental agents associated with MS susceptibility (vitamin D and the Epstein-Barr virus) influence B-cell proliferation and function. Finally, the only cell-specific treatments that are effective in patients with MS are monoclonal antibodies targeting the B-cell antigen CD20, suggesting a potentially causative role for B cells. Based on current evidence there is no longer doubt that B cells are relevant to the etiology and pathogenesis of MS. Elucidating the role of B cells in MS will be a fruitful strategy for disease prevention and treatment.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".