Dietary sources of five nutrients in ethnic groups represented in the Multiethnic Cohort
Bibliographic record
Abstract
Data are limited on how dietary sources of energy and nutrient intakes differ among ethnic groups in the USA. The objective of the present study was to characterise dietary sources of energy, total fat, saturated fat, protein, dietary fibre and added sugar for five ethnic groups. A validated quantitative FFQ was used to collect dietary data from 186,916 men and women aged 45-75 years who were living in Hawaii and Los Angeles between 1993 and 1996. Participants represented five ethnic groups: African-American; Japanese-American; Native Hawaiian; Latino; Caucasian. The top ten dietary sources of energy contributed 36·2-49·6% to total energy consumption, with rice and bread contributing the most (11·4-27·8%) across all ethnic-sex groups. Major dietary sources of total fat were chicken/turkey dishes and butter among most groups. Ice cream, ice milk or frozen yogurt contributed 4·6-6·2% to saturated fat intake across all ethnic-sex groups, except Latino-Mexico women. Chicken/turkey and bread were among the top dietary sources of protein (13·9-19·4%). The top two sources of dietary fibre were bread and cereals (18·1-22%) among all groups, except Latino-Mexico men. Regular sodas contributed the most to added sugar consumption. The present study provides, for the first time, data on the major dietary sources of energy, fat, saturated fat, protein, fibre and added sugar for these five ethnic groups in the USA. Such data are valuable for identifying target foods for nutritional intervention programmes and directing public health strategies aimed at reducing dietary risk factors for chronic disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".