Cross-reactivity between epidemiology and immunology in multiple sclerosis
Bibliographic record
Abstract
Studies in multiple sclerosis (MS) aimed at establishing the clinical relevance of circulating antibodies recognizing myelin epitopes, such as MOG or MBP, have produced inconsistent results. Explanations include differences in study design, study populations, assay methodologies, as well as the potential for antimyelin antibodies to emerge as a consequence of the immune response to injured CNS tissue, rather than being pathogenic. Wang et al.1 address whether there is increased risk of an MS diagnosis in asymptomatic individuals harboring anti-MOG antibodies and whether this may be explained by cross-reactivity between antibodies to MOG and the Epstein-Barr nuclear antigen (EBNA) epitope of EBV, previously implicated as a risk factor for MS. The authors conducted a nested case-control study using the powerful US Department of Defense Serum Repository (DoDSR), in which serum samples were collected prospectively from asymptomatic individuals. Samples from individuals who were subsequently diagnosed with MS were compared to samples from matched controls with regard to presence of anti-MOG antibodies. This nested approach provides a more robust epidemiologic …
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".