Consumption of dairy products and prevalence of depression and anxiety among adults
Bibliographic record
Abstract
No earlier study has examined the association between dairy intake and prevalence of depression and anxiety in Middle Eastern population. This cross-sectional study was done to investigate the association between consumption of total dairy intake and prevalence of depression and anxiety in a large group of adult population in Isfahan, Iran. Dairy intake was assessed for 3362 participants using a validated 106-item Willet-format dish-based semi-quantitative FFQ. A validated questionnaire of Hospital Anxiety and Depression Scale was used to examine depression and anxiety. We defined scores of > 8 as depressed and anxious people in this analysis. Information about covariates were collected using pre-tested questionnaires. Mean age of study population was 36.2, 58.3% of them were females. Participants in the highest quintile of dairy intake had a 40% lower chance for depression compared to those in the lowest quintile (OR = 0.60; 95%CI 0.47–0.76, P trend = 0.001) in crude model. This association remained significant after controlling for several confounders (0.57; 95%CI 0.40–0.80 P trend = 0.02). Although we observed a significant association between dairy intake and anxiety in crude model (OR: 0.63; 95%CI 0.46–0.87, P trend = 0.02), the association was not significant when we took into account potential confounders (0.63; 95%CI 0.39–1.00). We found an inverse association between consumption of dairy products and depression but the results for anxiety were not significant. Further studies, in particular of prospective nature, are recommended to confirm our findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".