Differential disease phenotypes and progression in relapsing–remitting multiple sclerosis: comparative analyses of single Canadian and Saudi Arabian clinics
Bibliographic record
Abstract
OBJECTIVE: Relapsing-remitting multiple sclerosis (RR-MS) phenotypes differ widely although the variables contributing to this heterogeneity remain uncertain. To assess geographic and ethnic effects on RR-MS phenotypes, we investigated RR-MS patients in Canada and Saudi Arabia. METHODS: A retrospective analysis of patients followed in two MS Clinics was performed in Medina, Saudi Arabia and Edmonton, Canada. Demographic and clinical data were collected for each patient and analyzed using univariable and multivariable statistics. Univariable and multivariable linear regression were used to distinguish the significant clinical and demographic features and neurological systems associated with the change in expanded disability status scale (EDSS) between clinical assessments. RESULTS: Patients with treated RR-MS were recruited (n = 51, Saudi; n = 47, Canada) although the disease duration was longer in the Canadian cohort (5.6 ± 2.2 yr.) compared to the Saudi cohort (4.4 ± 1.4 yr.) (P < 0.05), annual relapse rate and EDSS change were higher in the Saudi cohort (P < 0.05). Infratentorial lesion-associated presentation differed (Canada, n = 23; Saudi, n = 13) among groups (P < 0.05). Spinal cord lesions on MRI were more frequently detected in Canadian (n = 23) compared to Saudi (n = 1) patients (P < 0.05). Patients within the Saudi cohort displayed a significantly greater change in Expanded Disability Status Scale (EDSS) between first and second assessments. CONCLUSIONS: Despite differences in geographic location, ethnicity, and predominance of infratentorial lesions in the Canadian group, the RR-MS phenotypes were similar although the Saudi cohort displayed a more severe disease course.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".