Vitamin D in Multiple Sclerosis and Central Nervous System Demyelinating Disease—A Review
Bibliographic record
Abstract
BACKGROUND: The role of vitamin D as both a risk factor and a disease modifier in multiple sclerosis (MS) has a storied history with ongoing accumulation of supportive convergent evidence from animal data, clinical studies and trials, and biomarkers of disease. EVIDENCE ACQUISITION: A detailed review of the published literature ranging from in vivo immune studies to human clinical studies of epidemiology, physiology, immunology, clinical, and radiological markers was undertaken. RESULTS: There is compelling evidence that vitamin D is not only a risk factor for central nervous system (CNS) demyelinating disease (namely MS) but also seems to modify both the inflammatory and neurodegenerative elements of the disease, with large-scale treatment trials underway. The authors also address questions of interest that remain unanswered. CONCLUSIONS: Vitamin D is an important contributor and modifiable risk factor in CNS demyelinating disease. Further work will determine whether it is also neuroprotective and if such benefits will apply to other inflammatory and degenerative neurological diseases.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".