The role of potatoes and potato components in cardiometabolic health: A review
Bibliographic record
Abstract
Potatoes (Solanum tuberosum) are an important food crop worldwide and contribute key nutrients to the diet, including vitamin C, potassium, and dietary fiber. Potatoes and potato components have been shown to have favorable impacts on several measures of cardiometabolic health in animals and humans, including lowering blood pressure, improving lipid profiles, and decreasing markers of inflammation. A range of glycemic index (GI) values have been reported for potatoes, and data are sparse regarding the impact of potato consumption on the postprandial glycemic response, especially when potatoes are consumed with other foods. There is a lack of clinical trial data regarding the impact of potatoes on weight management. A small number of human cohort studies have reported beneficial associations between potato consumption as part of a healthy lifestyle and cardiometabolic health. Another small number of human population studies have included potatoes as part of a dietary pattern with other calorie-dense foods and have not reported cardiometabolic benefits. The epidemiological literature should be interpreted with caution due to lack of consistency in both defining dietary patterns that include potatoes and in control for potential confounding variables. Controlled clinical trials are needed to define the impact of potatoes on cardiometabolic health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".