Environmental factors in multiple sclerosis, focusing exposure to organic solvents
Bibliographic record
Abstract
Results obtained in previous studies on the relationship between exposure to organic solvents and risk of multiple sclerosis are not entirely consistent.The aim of this study was to investigate the relationship between exposure to organic solvents and risk of multiple sclerosis and possible interactions between exposure to solvents and smoking; exposure to solvents and infectious mononucleosis; exposure to solvents and D-vitamin.The case-control study on Evironmental Factors In Multiple Sclerosis (EnvIMS) was used.We examined two samples, the first one made up of 1.197 MS patients, and the second one, 2.361 healthy controls from Sweden and Norway; both group were matching in terms of their sex and age.We examined their exposure to organic solvents prior to the study; we also examined whether they had suffered from infectious mononucleosis, their outdoor activity (D-vitamin) and smoking status.The relationships between exposure to solvents and other risk factors were estimated as odds ratios (ORs) with 95 % confidence interval (95 % CI) using logistic regression.Exposure to organic solvents was found to be associated with an increased MS risk, OR 1.51 (95 % confidence interval (CI): 1.19-1.90;p = 0.001), adjusted OR was 1.36 (95 % CI: 1.05-1.75;p = 0.020).Adjusted ORs for different combinations including exposure to solvents and other risk factors such as smoking, infectious mononucleosis and D-vitamin showed an increased risk of MS.Among those who reported infectious mononucleosis there was no increased risk associated with exposure to solvents, OR 1.03 (95 % CI: 0.48-2.19).The increased risk seems to be present only in individuals with unfavorable smoking status and low D-vitamin status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".