Menstrual cycle symptoms are associated with nutrient intake: Results from network analysis from an online survey
Bibliographic record
Abstract
Background: Less is understood about female’s nutrient intake’s impact on the severity of the menstrual cycle (MC) symptoms, which consequently interferes with their life quality. Objectives: The goal of this study is to look at the relationship between female nutrient consumption and the severity of MC symptoms to better understand how food affects women’s quality of life during their MCs. Design: To investigate this impact among healthy adult women, a self-administered, cross-sectional online questionnaire was obtained from 204 regularly menstruating women aged between 18 and 40. Methods: The questionnaire included questions on sociodemographic characteristics, a semi-food frequency questionnaire (FFQ), Arabic Premenstrual Syndrome Scale (A-PMS-S) for MC symptoms. Results: Results showed intake of polyunsaturated fatty acids (PUFAs) was associated with lower no to mild versus moderate to severe physical symptoms (odds ratio (OR): 0.71, 95% confidence interval (CI): 0.59–0.85; p < 0.001), psychological symptoms (OR: 0.87, 95% CI: 0.77–0.99; p < 0.05), and functioning symptoms (OR: 0.92, 95% CI: 0.83–1.02; p > 0.1). Thiamine prevented psychological symptoms (OR: 0.02, 95% CI: 0.02–0.02; p < 0.001), physiological symptoms (OR: 0.59, 95% CI: 0.58–0.60; p < 0.001), and functioning symptoms (OR: 0.47, 95% CI: 0.47–0.48; p < 0.001). Saturated fat, iron, and niacin intakes increased the risk of experiencing MC psychological symptoms. Conclusion: Our findings suggest that MC symptoms were correlated with some nutrient intake from food sources, which is considered an external controllable factor more than demographic characteristics. Therefore, women should be aware of the type of food consumed during their monthly MC phase.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".