Clinical and MRI characteristics of multiple sclerosis in patients of Middle Eastern and North African ancestry residing in Ontario, Canada
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) incidence is rising in traditionally low-burden regions, including the Middle East and North Africa (MENA). Objectives: Our objective was to evaluate disease characteristics in MS patients of MENA descent (MENA-MS). Methods: MENA-MS patients and age- and sex-matched MS patients of European descent (EUR-MS) were identified through the MS Clinic Registry of St. Michael’s Hospital in Toronto, Canada. Disease activity and severity were evaluated by the annualized relapse rate (ARR), magnetic resonance imaging (MRI) activity, change in the Expanded Disability Status Scale (EDSS), progression index (PI), and MS Severity Score (MSSS). Results: All MS patients within the registry identified to be of MENA origin ( n = 192), and age- and sex-matched EUR-MS patients were included. Mean age was 42.9 years, 67% female. A total of 25% and 24% of EUR-MS and MENA-MS had progressive disease, with similar mean disease durations (11.5 and 11.4 years, respectively). Clinical and radiological disease activity (ARR, proportion with new/enlarging MRI lesions) was similar. MENA-MS showed greater disability progression over time (EDSS change = 0.24 vs. 0.06, p = 0.01), a higher MSSS (3.12 vs. 2.67, p = 0.04), and higher PI (0.34 vs. 0.27, p = 0.07). Conclusion: MENA-MS patients demonstrate higher disease severity compared to EUR-MS patients, despite having similar inflammatory measures of disease activity, with disability progression in the absence of relapses. These observations illustrate the importance of the intersections of environmental, socioeconomic, and genetic determinants in optimizing individualized MS care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".