Nut consumption is associated with a healthy dietary pattern.
Bibliographic record
Abstract
The objective of the research was to determine the relation between nut consumption and dietary patterns described by Healthy Eating Index, Mediterranean Diet Score and principal component analysis. In a cross-sectional study, 1852 military men were contacted by mail. Using food-frequency questionnaires, nut consumption was recorded and stratified in weekly versus less than weekly. Three dietary indices were calculated and stratified in quintiles. For principal component analysis, the healthiest dietary pattern rich in fruits and vegetables was selected as Healthy Dietary Pattern. The highest quintiles of Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern were systematically associated with the highest weekly consumption of nuts. The highest quintiles were also associated with the lowest intake of saturated fat, i.e. between 10 and 12 energy-percent compared with 17 to 19 energy-percent for the lowest quintiles. The mean daily nut consumption was less than 6 g a day, which is beneath the recommended quantity for cardiovascular protection. Nut consumption was associated with the healthiest dietary pattern, independently of the used method to determine the dietary pattern. Regular nut consumption seems to be a component of a cluster of several healthy behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".