Dietary Patterns and Premenstrual Syndrome: A Cross-Sectional Study
Bibliographic record
Abstract
This study investigated the cross-sectional association between major dietary patterns and premenstrual syndrome (PMS). Participants were 427 women aged 22-50 y who responded to a mail survey in 2022-2023. Dietary patterns were derived using principal component analysis based on consumption of 52 food and beverage items ascertained by a validated self-administered diet history questionnaire. PMS were assessed using the Premenstrual Symptoms Questionnaire. Logistic regression analysis was used to estimate odds ratios of PMS according to tertiles of dietary pattern scores. The prevalence of moderate to severe PMS was 9.6% (41 women). We identified four dietary patterns: vegetable, tomato and fish, Japanese, and alcohol dietary patterns. No dietary pattern was significantly associated with PMS. However, the odds ratios of PMS in the highest tertile of the vegetable dietary pattern (characterized by high intake of vegetables, mushrooms, potatoes, seaweeds, and chicken) tended to be lower compared to the lowest tertile. The multivariable-adjusted odds ratio of PMS for the highest versus lowest tertile of the vegetable dietary pattern score was 0.69 (95% confidence interval 0.30-1.59). None of the dietary patterns were appreciably associated with PMS. The finding of lower odds of PMS among women with a high score for the vegetable dietary pattern deserves further investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".