CAN MONTH OF BIRTH AND UV RADIATION AFFECT MULTIPLE SCLEROSIS RISK IN PROVINCES OF IRAN?
Bibliographic record
Abstract
Introduction: Multiple sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system that affects women more than men. Environmental factors such as sunlight, through Ultraviolet (UV) radiation can have a pivotal role in MS prevalence. MS is more common in mid latitude temperate climatic areas, such as the northern United States, southern Canada and northern Europe. It has been shown that there is a low risk of MS in the hot, moist and extremely dry equatorial zone, and vice versa. Materials and methods: In this study, UV radiation data as UV index were collected from a geographic database provided by The Environmental Health Information System of Islamic Republic of Iran. The monthly average UV index during pregnancy leading to 15th of each month has been used to create GIS maps by using ArcGIS 9.3. Results: In this paper, It was focused on the potential effect of UV radiation on MS prevalence. It was found that there is a low maternal exposure to UV radiation in people born in April and May in all provinces of Iran and they can be more susceptible to MS. Conclusions: This study shows that Iranian high exposure to UV radiation can reduce the risk of MS. It was indicated that Iran is a low risk area for MS and people born in April and May are more susceptible to MS due to low exposure to UV radiation. Thus, birth months could be considered as an important factor in MS prevalence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".