Food Polyamine and Cardiovascular Disease -An Epidemiological Study-
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to examine the contribution of dietary polyamines toward preventing cardiovascular disease (CVD). Age-standardized mortality rates as well as other relevant information regarding individuals with CVD were gathered from the World Health Organization and the International Monetary Fund in 48 different European and other Western countries. Food supply data were collected from the database of the United Nations, and the amount of dietary polyamines was estimated by using polyamine concentrations in foods from published sources. The association between CVD mortality and the amount of polyamines was investigated by performing a series of multiple linear regression analyses. Analyses using factors known to modulate the risk of CVD including: Gross Domestic Product (GDP) (standardized regression coefficient (r) = -0.786, p < 0.001) and the amount of fruits, vegetable, nuts, and beans (r = -0.183, p = 0.001) but not including polyamines, showed negative associations with CVD, while smoking rate (r = 0.139, p = 0.041) and whole milk amount (r = 0.131, p = 0.028) showed positive associations with CVD. When the amount of polyamines was added to the analyses as a covariate, GDP (r = -0.864, p < 0.001) and polyamines (r = -0.355, p = 0.007) showed negative associations with CVD, while smoking rate (r = 0.183, p = 0.006) and whole milk (r = 0.113, p = 0.041) showed positive associations with CVD. The inverse association between dietary polyamines and CVD mortality revealed by the present study merits further evaluation.
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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 | 0.003 | 0.000 |
| 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 | 0.000 |
| 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 it