Is high socioeconomic status a risk factor for multiple sclerosis? A systematic review
Bibliographic record
Abstract
High socioeconomic status (SES) is generally associated with better health outcomes, but some research has linked it with an increased risk of multiple sclerosis (MS). The evidence for this association is inconsistent and has not previously been systematically reviewed. A systematic review of cohort and case-control studies in any language was conducted looking at the association between MS and SES. MEDLINE and EMBASE were searched for articles in all languages published up until 23 August 2013. Twenty-one studies from 13 countries were included in the review. Heterogeneity of study settings precluded carrying out a meta-analysis, and a qualitative synthesis was performed instead. Five studies, all from more unequal countries, reported an association between high SES and MS. Thirteen studies reported no evidence of an association, and three studies reported an association with low SES. These 16 studies largely came from more egalitarian countries. The evidence for an association between high SES and increased MS risk is inconsistent but with some indication of a stronger effect in countries and time periods with higher inequality. Firm conclusions are hampered by the failure of most studies to control for other important risk factors for MS.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".