Bibliographic record
Abstract
Background: The prevalence of multiple sclerosis (MS) in Iran is increasing. Although diet is an important modifiable risk factor for MS, the cumulative impact of micronutrients on MS is not yet fully understood . The objective was t o evaluate the association between nutrient pattern and the risk of MS . Methods: A validated food frequency questionnaire was used in the hospital-based case-control study. Sixty-eight patients with newly diagnosed MS and 140 controls were included. Results: Cases and controls were selected from the Sinai Hospital in Tehran. Conducting principal component on 20 nutrients, four main nutrient patterns were revealed. Factor 1 included thiamin, selenium, niacin, copper and magnesium. Factor 2 was characterized by high riboflavin, calcium, vitamin D, zinc, linolenic acid and caffeine. Factor 3 was high in polyunsaturated fatty acids, monounsaturated fatty acids, alpha tocopherol, vitamin E and saturated fatty acids, and factor 4 was characterized by high loadings of vitamin C, β carotene and vitamin A. Using unconditional logistic regression, factors 2 and 4 were inversely associated to MS risk (OR = 0.25 (0.11 - 0.58) and OR = 0.43(0.21 - 0.87) respectively ). Factors 1 and 3 showed no significant association with MS. Conclusion: Findings suggested that nutrient patterns may be important in etiopathogenesis of MS and may offer new approaches to prevent MS. J Neurol Res. 2014;4(2-3):72-80 doi: http://dx.doi.org/10.14740/jnr277w
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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) | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".