The Association of Nutrient Patterns and Risk of Ulcerative Colitis: A Case-Control Study.
Bibliographic record
Abstract
Background and aim: Nutrient pattern approach is an appropriate way to compare nutrient intakes across different populations due to the universality of nutrients' nature. The current study was purposed to examine the association between patterns of nutrient intakes and risk of ulcerative colitis (UC) among Iranian adults. Methods: In this case-control study, we enrolled 109 UC patients and 218 age- and sex-matched controls. Dietary intakes were assessed using a validated self-administered 106-item dish-based Food Frequency Questionnaire (FFQ). We also used a pre-tested questionnaire to collect data on potential confounders. A gastroenterology specialist made the diagnosis of UC according to international criteria. Results: In total, 2 nutrient patterns were identified using factor analysis. We found the first nutrient pattern (NP1), characterized by the high intakes of macronutrients, B-vitamins, selenium, iron, zinc, sodium, phosphorus, manganese, magnesium, copper, calcium, fiber, and vitamins E and D, was inversely associated with odds of UC. This association remained significant after taking potential confounders into account; individuals in the top tertile of NP1 score had 93% lower odds of UC compared with those in the bottom tertile (OR: 0.07, 95% CI, 0.01-0.32). Regarding NP2, containing a high amount of beta-carotene, vitamins A, K, and C, potassium, and folate, a significant inverse association was also found (OR: 0.19, 95% CI, 0.09-0.38); such that in the fully adjusted model, individuals in the third tertile of NP2 score were 64% less likely to have UC compared with those in the first tertile (OR: 0.36, 95% CI, 0.15-0.82). Conclusion: We found that a dietary pattern rich in antioxidants, B-vitamins, macronutrients, zinc, iron, copper, calcium, potassium, fat-soluble vitamins, and fiber is inversely associated with UC.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".