Spatial Analysis of Global Prevalence of Multiple Sclerosis Suggests Need for an Updated Prevalence Scale
Bibliographic record
Abstract
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system with an unknown aetiology. MS has a geographic pattern of prevalence with high prevalence rates between 45 degrees and 65 degrees north. In much of the northern hemisphere, there exists a prevalence gradient, with increasing prevalence from south to north. While genetics may partially explain the latitudinal gradient, it is not strong enough to exclude exogenous variables. Kurtzke initially came up with a three-zone scale for low, medium, and high prevalence zones. He defined high as 30 or more per 100,000, medium as 5-29 per 100,000, and low as less than 5 per 100,000. In this study, 131 geographic datasets (geocases) were spatially analyzed to determine whether the existing global prevalence scale needed to be updated. The mean prevalence rate was 67.83/100,000 with rates ranging from 350/100,000 to 0/100,000. The results of this study suggest that the commonly referenced scale for global MS prevalence needs to be updated with added zones to reflect significantly higher prevalence rates in some areas of the world. We suggest a five-zone scale: very high (170-350), high (70-170), medium (38-70), low (13-38), and very low (0-13).
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".