Carbohydrates, dietary glycaemic load and glycaemic index, and risk of acute myocardial infarction
Bibliographic record
Abstract
OBJECTIVES: To assess the relation between selected carbohydrate foods, dietary glycaemic load and glycaemic index, and the risk of non-fatal acute myocardial infarction in a population with a high intake of refined carbohydrates. DESIGN AND SETTING: Hospital based case-control study conducted in Milan, Italy, between 1995 and 1999. PATIENTS: 433 non-diabetic subjects with a first episode of non-fatal acute myocardial infarction, and 448 controls admitted to hospital for a wide spectrum of acute conditions unrelated to known or potential risk factors for acute myocardial infarction. METHODS: Information was collected by interviewer administered questionnaires. Multivariate odds ratios (OR) and 95% confidence intervals (CI) were obtained by multiple logistic regression models. RESULTS: Compared with patients in the lowest tertile of intake, the multivariate OR for those in the highest tertile was 1.00 for bread, 1.27 for pasta and rice, 1.38 for soups, 0.78 for potatoes, 0.97 for desserts, and 1.00 for sugar. The OR for the highest tertile of score was 1.08 for glycaemic load and 1.38 for glycaemic index. None of the estimates was significant. A significant association with acute myocardial infarction risk was found for glycaemic index in patients aged > or = 60 years (OR 1.81, 95% CI 1.07 to 3.07 for the highest tertile of score compared with the lowest) and in those with a body mass index > or = 25 kg/m2 (OR 2.02, 95% CI 1.21 to 3.34). CONCLUSIONS: In this Italian population high glycaemic load and glycaemic index were not strongly associated with acute myocardial infarction risk, but slightly increased odds ratios were observed for glycaemic index in elderly people and in association with overweight.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".