Food group sources and intake of long‐chain fatty acids in the Adventist Health Study‐2 cohort (810.30)
Bibliographic record
Abstract
Long chain fatty acids have unique and varying effects on health. Identifying common dietary sources of fatty acids can help in developing individual and public health recommendations regarding intake. The purpose of this descriptive study is to identify the food group sources of long‐chain fatty acids in the Adventist Health Study‐2 cohort. This cohort has approximately 96,000 participants (65% female; 26% Black; mean age 58.2 years) from the U.S. and Canada. A quantitative 204 food‐item food frequency questionnaire (FFQ) was used to measure food intake. The FFQ was used to estimate fatty acid intake, identify 14 major and 51 minor food groups, and define dietary patterns (vegan (7.6%), vegetarian (28.9%), semi‐vegetarian (5.5%), pesco‐vegetarian (9.8%), and non‐vegetarians (48.2%)). Preliminary results show mean dietary intake (per 2000 kcal) of 72.1 g total fat, 17.7 g linoleic acid, 0.02 g arachidonic acid, 1.85 g α‐linolenic acid (ALA), 16 mg of eicosapentaenoic acid and 39 mg docosahexaenoic acid. Estimated ALA (0.8% kcal) and linolenic acid (8% kcal) intake meets the dietary reference intake of 0.6‐1.2 and 5‐10% kcals, respectively. Average dietary EPA+DHA intake in this cohort is less than the American Heart Association recommendation of 0.5‐1 g for prevention of heart disease. Further analyses will determine food sources of these fatty acids within each dietary pattern.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".