Exploring the link between autoimmune disorders and the risk of developing multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system which leads to demyelination and neurodegeneration.While the cause of MS remains unknown, current research points to key genetic, environmental, and infectious factors which play a role in the onset of disease.The aim of the research undertaken in this thesis was to investigate the possible role of autoimmune disorders (AiDs) in the etiology of MS and to determine whether specific AiDs confer an increased risk for MS.The AiDs examined in this thesis are rheumatoid arthritis (RA), type-1 diabetes (T1D), psoriasis, Crohn's disease (CD), ulcerative colitis (UC), systemic lupus erythematosus (SLE), celiac disease, hypothyroidism, and hyperthyroidism.Published studies yielded conflicting results; some studies found that T1D, psoriasis, CD, SLE, and hypothyroidism were associated with an increased risk of MS, while others found no evidence of an association with MS.The association between AiDs and the risk of MS was studied using data from the Canadian, Italian, and Norwegian components of the Environmental Risk Factors in Multiple Sclerosis (EnvIMS) study, a multi-national case-control study.Cases (N = 2,242) were frequency matched to controls (N = 3,992) on sex and age in each country.Three exposure windows were defined to assess the association between the AiDs and MS; exposure window one (EW1) was the diagnosis of the AiD any time prior to MS, exposure window two (EW2) required a minimum 5-year time lag between the diagnosis of the AiD and MS, and exposure window 3 (EW3) only included AiDs diagnosed at age 18 years or younger.The association between the AiDs and MS in each exposure window was explored in two ways: 1) the association between having any AiD and the risk of MS, and 2) the association between each of the AiDs and the risk of MS (for EW1 and EW2 only), where numbers were sufficient to permit such analyses.The
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".