Disease phenotype analyses of relapsing-remitting multiple sclerosis in Canada and Saudi Arabia
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) exhibits a spectrum of clinical findings, especially in relapsing-remitting MS (RR-MS). To assess the effects of geographic location and ethnicity on RR-MS phenotype, we investigated RR-MS patients in Canada and Saudi Arabia. Methods: A retrospective cross-sectional analysis of patients receiving active care in MS Clinics was performed in Medina, Saudi Arabia and Edmonton, Alberta. Demographic and clinical data was collected for each patient. Results: 98 patients with treated RR-MS were recruited (n=51, Medina; n=47, Edmonton); 40 patients were Caucasian (Edmonton) while 46 patients were Bedouin (Medina). Although the disease duration was longer in the Edmonton (5.7+2.3 yr) compared to the Medina group (4.4+1.4 yr) (p<0.05), the mean age of RR-MS onset, relapse rate and EDSS change were similar. The female:male ratio was comparable in Edmonton (35:12) and Medina (32:19), as was the risk of optic neuritis. The likelihood of an infratentorial lesion-associated presentation differed (Edmonton, n=23; Medina; n=13) among groups (p<0.05). Spinal cord lesions on MRI were more frequent in Edmonton (n=18) compared to Medina (n=1) patients (p<0.05). Conclusions: Despite differences in location, ethnicity, and a predominance of infratentorial lesion burden the Edmonton group, the RR-MS phenotype displayed similar disease severity and trajectory in these cohorts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".