The Prevalence of Multiple Sclerosis in the Middle East
Bibliographic record
Abstract
BACKGROUND: The prevalence of multiple sclerosis (MS) in the Middle East has been reported to be low to medium. METHODS: To verify this assertion we conducted a review of published data on the occurrence of MS in the Middle East. RESULTS: Fourteen studies reporting on the prevalence of MS in the Middle East were initially identified, 5 of which were excluded due to inadequate data or serious methodological limitations. The data from the 9 included studies suggested that the prevalence of MS may vary widely within the Middle East, from low to high. However, these 9 studies were inconsistent in case ascertainment, inclusion criteria and methods of prevalence calculation, and most did not include age/sex standardization. CONCLUSION: Methodological inconsistencies among studies make it difficult to be confident in drawing conclusions about the prevalence of MS in the Middle East. Nevertheless, there is little evidence to support the assertion that the prevalence of MS in the Middle East is low to medium. Rather, the prevalence of MS in the Middle East may range from low to high, depending on the specific population and environment of study. However, to confirm these findings, further epidemiological research is needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".