Diet quality and risk of multiple sclerosis in two cohorts of US women
Bibliographic record
Abstract
Objective: To determine the association between measures of overall diet quality (dietary indices/patterns) and risk of multiple sclerosis (MS). Methods: Over 185,000 women in the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII) completed semiquantitative food frequency questionnaires every 4 years. There were 480 MS incident cases. Diet quality was assessed using the Alternative Healthy Eating Index-2010 (AHEI-2010), Alternate Mediterranean Diet (aMED) index, and Dietary Approaches to Stop Hypertension (DASH) index. Principal component analysis was used to determine major dietary patterns. We calculated the hazard ratio (HR) of MS with Cox multivariate models adjusted for age, latitude of residence at age 15, body mass index at age 18, supplemental vitamin D intake, and cigarette smoking. Results: None of the dietary indices, AHEI-2010, aMED, or DASH, at baseline was statistically significantly related to the risk of MS. The principal component analysis identified “Western” and “prudent” dietary patterns, neither of which was associated with MS risk (HR, top vs bottom quintile: Western, 0.81 ( p = 0.31) and prudent, 0.96 ( p = 0.94)). When the analysis was repeated using cumulative average dietary pattern scores, the results were unchanged. Conclusion: There was no evidence of an association between overall diet quality and risk of developing MS among women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".