Bibliographic record
Abstract
Evidence that environmental factors play an important role in the etiology of multiple sclerosis (MS) has grown rapidly over the last few years. In particular, a role for the Epstein-Barr virus (EBV) has been gaining credibility and momentum on the crest of robust epidemiologic data demonstrating an extremely low MS risk among the noninfected,1 a strong correlation between serum antibody titers to the EBV nuclear antigen-1 and future risk of developing MS in both adult and pediatric onset disease,2–6 and a twofold to threefold increase in MS risk following the occurrence of infectious mononucleosis,7,8 a common clinical manifestation of a late primary EBV infection. More recently, in the longitudinal follow-up of a large cohort of EBV-negative young adults, MS was observed to occur only after EBV infection.9 Over the years, lack of convincing evidence of EBV within the CNS10–12 of patients with MS suggested that indirect mechanisms mediated any pathogenic effect of EBV.13 The indirect mechanisms posited are varied, including T-cell activation potentially through molecular mimicry or transactivation of a superantigen, a class of antigens that can activate large populations of T cells nonspecifically14–16; infection and chronic activation of autoreactive B cells17; or induction and presentation of antigens such as α-β crystalline, a stress protein that is a major putative target of CD4+ T-cell immunity in MS.18 In 2007, however, Serafini et al.19 reported in a study of 22 MS brains that numerous B cells in ectopic meningeal follicles (collections of immune cells which recapitulate some architectural features of lymph node follicles) and in perivascular infiltrates were positive by in situ hybridization for EBV RNA transcripts (EBER) and by immunohistochemistry for EBNA-2 and LMP-1, 2 EBV latent proteins …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".