Does low socioeconomic status in early life protect against multiple sclerosis? A multinational, case–control study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The findings from existing research on the association between socioeconomic status (SES) and multiple sclerosis (MS) are inconsistent. Most previous studies are limited to one country and do not adequately adjust for other risk factors for the disease. METHODS: The association between SES and MS was examined using data from the multinational Environmental Risk Factors in Multiple Sclerosis (EnvIMS) case-control study, comprising 2144 cases and 3859 controls from Norway, Canada and Italy. Multiple logistic regression was used to estimate the odds ratios and 95% confidence intervals for the association between early life SES, measured by parental educational level, and MS. Analyses were adjusted for age, sex, sunlight exposure, history of infectious mononucleosis, smoking, obesity and family size. RESULTS: Relative to those whose parents had primary school education or below, the adjusted odds ratio (95% confidence interval) for MS amongst individuals with university-educated parents, and the P value for trend across education levels, were 1.45 (1.03-2.05) in Canada (P for trend 0.030), 1.09 (0.85-1.39) in Norway (P for trend 0.395) and 0.65 (0.39-1.07) in Italy (P for trend 0.158). CONCLUSION: There is no consistent association between parental SES and MS risk in Norway, Canada and Italy, with a protective effect of low SES only found in Canada.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".