Bibliographic record
Abstract
Over 2 million persons worldwide have multiple sclerosis (MS),1 and the burden of the disease for affected individuals and society is substantial. A recent study estimated that by 2031, 133,635 Canadians would be living with MS, and that the direct costs of their care would reach a staggering $2 billion annually.2 Therefore, identifying modifiable risk factors for MS remains vitally important. Researchers still seek to firmly demonstrate a causal role for vitamin D, a biologically plausible etiologic factor which is modifiable. Vitamin D receptors are ubiquitous, being expressed on immune cells and in the CNS; immune responses are affected by variations in 25-hydroxyvitamin D (25[OH]D) levels; and 1,25-dihydroxycholecaliferol can prevent the emergence of experimental autoimmune encephalomyelitis,3,4 an animal model of demyelinating disease. However, epidemiologic studies have often been hindered by the inability to demonstrate temporality; that is, that the exposure to inadequate 25(OH)D occurred before the onset of MS.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".