Comparison of Dietary Intake between Polycystic Ovary Syndrome Women and Controls
Bibliographic record
Abstract
Polycystic Ovary syndrome (PCOS) is a complicated endocrinopathy affecting women in reproductive age. The crucial role of obesity and insulin resistance in progression of metabolic and cardiovascular features of PCOS has been confirmed. Although it has been suggested that there is a possible association between dietary pattern and risk of PCOS, few studies investigating the diet composition of PCOS women. The aim of this study was to compare the dietary intakes between women with polycystic ovary syndrome (PCOS) and eumenorrheic non hirsute women. This was a case control study of 142 women with PCOS and 140 eumenorrheic non hirsute healthy age and BMI matched controls. We compared the dietary intakes of our study group using a validated food frequency questionnaire (FFQ), using T-test or Mann-Whitney to compare the means of two groups. One way Anova was used to compare the tertiles of GI and GL in each group and a two way ANOVA was used to compare between tertiles of GI-GL and groups. The results demonstrated that energy and macronutrient intakes in PCOS women compared to controls were similar. PCOS group consumed more food items with high glycemic index (p=0.042) and less legumes (P=0.026) and vegetables (p=0.037) than controls. Both groups in the highest tertile of glycemic load (GL) had higher body mass index and waist circumference. Considering the results of this study, it was concluded that PCOS women had a dietary pattern that was characterized by a higher consumption of high GI food items and lower legumes and vegetables.
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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) | 0.002 | 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".