A Quantitative Analysis of Suspected Environmental Causes of MS
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a disease with purported environmental causes. Consistent correlations have been found in various settings for latitude, smoking exposure, sunlight, and vitamin D deficiency. We analysed the contribution of various environmental factors to the risk of developing MS from a population perspective. METHODS: We collated global data of MS prevalence from 54 studies over the previous ten years and calculated the degree of risk contributed by latitude, longitude, ultraviolet radiation (from NASA satellite data and formulae for available sunlight hours), population smoking rates (from WHO data), gender, study date, study demographics, and several socioeconomic factors. We report a very significant negative correlation between MS prevalence and available ultraviolet (UV) radiation. RESULTS: The lack of available UV radiation outweighs other factors by at least 20 fold (p < 10⁻⁸) from single variate regression analysis. Multiple regression analysis revealed that latitude and longitude are also significant factors; smoking may also provide a very minimal role. The eight prevalence studies from Scandinavia produced prevalences that were lower than expected, given their global geospatial positioning. CONCLUSIONS: The available ultraviolet radiation is a significant environmental factor, more so than all the other factors examined.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".