Lipid, protein and carbohydrate intake in relation to body mass index: an Italian study
Bibliographic record
Abstract
OBJECTIVE: To analyse the association between macronutrient intake and body mass index (BMI). DESIGN: A series of hospital-based case-control studies. SETTINGS: Selected teaching and general hospitals in several Italian regions. SUBJECTS: A total of 6619 subjects from the comparison groups of the case-control studies were included in the analysis. METHODS: We obtained data from a validated 78-item food-frequency questionnaire submitted between 1991 and 2002. For various macronutrients, the partial regression coefficient (variation of BMI (kg m(-2)) per 100 kcal increment of energy intake) was derived from multiple linear regression models, after allowance for age, study centre, education, smoking habits, number of eating episodes and mutual adjustment for macronutrients. RESULTS: BMI was directly associated with protein intake among women only (beta = 0.68) and with unsaturated fats in both genders (for monounsaturated fats beta = 0.27 for men and 0.26 for women; for polyunsaturated fats beta = 0.27 for men and 0.54 for women), and inversely related to carbohydrates (beta = -0.05 for men and -0.21 for women) and number of eating episodes in both genders (beta = -0.42 for men and -0.61 for women) and to saturated fats among women only (beta = -0.57). CONCLUSIONS: These results confirm and provide convincing evidence that, after allowance for selected covariates including total energy intake, a protein-rich diet is not inversely related to BMI, and a carbohydrate-rich diet is not directly related to BMI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".