Association between Different Types of Edible Oils and Anthropometric Indices Mood, and Appetite among Women
Bibliographic record
Abstract
Introduction: The objective of this study was to evaluate the relationship between consumption of dietary oils and anthropometric indices, mood, and appetite among women staff of Tehran University of Medical Sciences. Methods: A cross-sectional study design was used, and 245 women staff of Tehran University of Medical Sciences participated. A 168-item food frequency questionnaire was used to evaluate dietary and nutrient intake. The association between liquid vegetable oils, hydrogenated vegetable oil, and animal fat intake and anthropometric indices, appetite, and mood was evaluated. The Profile of Mood States (POMS) questionnaire was used to assess mood. A Visual Analogue Scale (VAS) was used to evaluate appetite status. The tape measure was used to measure the waist circumference and height. SPSS was used to compute body mass index (BMI) and waist-to-height ratio (WHtR). Results: trend = 0.025) of a high appetite compared to those with a low intake of MUFA. However, the study found no evidence of an association between consumption of edible oils (vegetable oils, animal fat oils, and other fatty acid sources) and mood, anthropometric indices, or appetite. Conclusions: In the current research, we noticed a significant connection between moderate intake of MUFA and a large appetite and no association between consumption of edible oils and other outcomes. In conclusion, a balanced diet low in fast meals, processed foods, cakes, cookies, and sweets is suggested to limit the consumption of artificial trans-fatty acids.
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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 | 0.000 | 0.000 |
| 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".