Risk Factors Associated with the Onset of Relapsing-Remitting and Primary Progressive Multiple Sclerosis: A Systematic Review
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic central nervous system disease with a highly heterogeneous course. The aetiology of MS is not well understood but is likely a combination of both genetic and environmental factors. Approximately 85% of patients present with relapsing-remitting MS (RRMS), while 10-15% present with primary progressive MS (PPMS). PPMS is associated with an older onset age, a different sex ratio, and a considerably more rapid disease progression relative to RRMS. We systematically reviewed the literature to identify modifiable risk factors that may be associated with these different clinical courses. We performed a search of six databases and integrated twenty observational studies into a descriptive review. Exposure to Epstein-Barr virus (EBV) appeared to increase the risk of RRMS, but its association with PPMS was less clear. Other infections, such as human herpesvirus-6 and chlamydia pneumoniae, were not consistently associated with a specific disease course nor was cigarette smoking. Despite the vast literature examining risk factors for the development of MS, relatively few studies reported findings by disease course. This review exposes a gap in our understanding of the risk factors associated with the onset of PPMS, our current knowledge being predominated by relapsing-onset 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.011 |
| 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) | 0.004 | 0.000 |
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".