Bibliographic record
Abstract
A strong link has been made between dietary content and cardiac disease risk. Diets high in fruits, vegetables, whole grains, fish and poultry and lower in red meat have been shown to lower cardiac disease risk in both women and men. National diet guidelines, such as the Canada’s Food Guide (CFG), provide information on basic healthful eating. The CFG, however, lacks the details that are recommended in several cardiac disease-specific diets. The Alternate Health Eating Index (AHEI) is a scoring index that accounts for specific dietary factors such as types of fat, forms of carbohydrates and specific protein sources. High levels of adherence to the AHEI are associated with significantly lower cardiovascular disease risk in both men and women. This study evaluated dietary pattern for cardiac participants over a 16 month period; AHEI score and CFG adherence were measured, AHEI trends over time were examined and differences in AHEI scores based on sex, education level and income were examined. There was moderate correlation between the AHEI and CFG scores (r= 0.73, p=0.001). There were no significant changes over time for either food score and no sex differences noted. Participants with an education level greater than high school had significantly higher AHEI scores at baseline. Intake of fruits and vegetables did not meet recommended amounts at any time, though fibre intake well exceeded the recommendations for both men and women. Future evaluation of patients who receive formal cardiac rehabilitation may improve understanding of how the AHEI can be used as a tool for dietary evaluation in cardiac patients.
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) | 0.002 | 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".