<i>Insulin Resistance:</i> Concepts, Controversies, and the Role of Nutrition
Bibliographic record
Abstract
Insulin resistance is a prevalent condition, in which insulin loses its normal physiological action. Since people were first classified as insulin resistant over 60 years ago, one of the main discoveries has been that insulin resistance clusters with other risk factors such as obesity, elevated triglycerides, and low high-density lipoprotein cholesterol, increasing cardiovascular disease risk. Although insulin resistance appears to manifest first in the periphery and then in the liver, other sites, such as the brain and the pancreatic &b.beta-cell, may play pathogenic roles. Factors contributing to insulin resistance at these sites include perturbations in free fatty acids, glucose, and hormone-signalling, some of which may be linked to various genetic polymorphisms. Appropriate nutritional treatment for insulin resistance is controversial. Two main approaches are drawn from diabetes recommendations: i) a high-carbohydrate, low-fat, high-fibre diet emphasizing low glycemic-index foods and ii) sharing calories between monounsaturated fat and complex carbohydrate at the expense of saturated fat. Recent interest in insulin resistance has prompted the development of new guidelines. Promising data have also emerged, showing that a high-carbohydrate, high-fibre, low-fat diet plus exercise programs maintained through intensive counselling can decrease diabetes risk by over 40%. Additional research is required to confirm the sustainability of this approach and sort out the determinants of insulin resistance so that more effective nutritional interventions will result.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".