Bibliographic record
Abstract
Emerging data point to important contributions of both autoimmune inflammation and progressive degeneration in the pathophysiology of multiple sclerosis (MS). Unfortunately, after decades of intensive investigation, the fundamental cause remains unknown. A large body of research on the immunobiology of MS has resulted in a variety of anti-inflammatory therapies that are highly effective at reducing brain inflammation and clinical/radiological relapses. However, despite potent suppression of inflammation, benefit in the more important and disabling progressive phase is extremely limited; thus, progressive MS has emerged as the greatest challenge for the MS research and clinical communities. Data obtained over the years point to a complex interplay between environment (e.g., the near-absolute requirement of Epstein-Barr virus exposure), immunogenetics (strong associations with a large number of immune genes), and an ever more convincing role of an underlying degenerative process resulting in demyelination (in both white and grey matter regions), axonal and neuro-synaptic injury, and a persistent innate inflammatory response with a seemingly diminishing role of T cell-mediated autoimmunity as the disease progresses. Together, these observations point toward a primary degenerative process, one whose cause remains unknown but one that entrains a nearly ubiquitous secondary autoimmune response, as a likely sequence of events underpinning this disease. Here, we briefly review what is known about the potential pathophysiological mechanisms, focus on progressive MS, and discuss the two main hypotheses of MS pathogenesis that are the topic of vigorous debate in the field: whether primary autoimmunity or degeneration lies at the foundation. Unravelling this controversy will be critically important for developing effective new therapies for the most disabling later phases of this disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".