The Association between Index of Nutritional Quality (INQ) and Obesity: Baseline Data of Kharameh Cohort
Bibliographic record
Abstract
Obesity is an increasing problem that can lead to noncommunicable diseases. The role of dietary factors on one's obesity is confirmed in many studies. One nutritional approach that can be used for assessment of the foods and diets is the Index of Nutritional Quality (INQ). Our study is aimed at exploring the association between INQ and obesity. Our hypothesis is that enriched and high-quality diets reduce the risk of overweight or obesity. This study was carried out on 6248 overweight and obese participants, from whom 4356 (69.7%) and 1892 (30.3%) were overweight and obese, respectively. To assess the dietary intake for the participants, a valid food frequency questionnaire (FFQ) with 130 food items was utilized. The analysis revealed an inverse association between the overweight and the INQ of iron, thiamin, riboflavin, B6, folate, zinc, magnesium, calcium, and vitamin C and E. For the obese group, this inverse association was found for iron, B6, folate, zinc, magnesium, calcium, and vitamin C and E. These results approved our hypothesis that a rich nutrition diet may lead to a lower risk of obesity.
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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 | 0.007 | 0.002 |
| 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 | 0.001 |
| 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".