Contribution of food groups to energy, grams and nutrients-to-limit: the Latin American Study of Nutrition and Health/Estudio Latino Americano de Nutrición y Salud (ELANS)
Bibliographic record
Abstract
OBJECTIVE: To quantify the energy, nutrients-to-limit and total gram amount consumed and identify their top food sources consumed by Latin Americans. DESIGN: Data from the Latin American Study of Nutrition and Health (ELANS). SETTING: ELANS is a cross-sectional study representative of eight Latin American countries: Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru and Venezuela. PARTICIPANTS: Two 24-h dietary recalls on non-consecutive days were used to estimate usual dietary intake of 9218 participants with ages between 15-65 years. 'What We Eat in America' food classification system developed by United States Department of Agriculture was adapted and used to classify all food items consumed by the ELANS population. Food sources of energy, added sugars, SFA, Na and total gram amount consumed were identified and ranked based on percentage of contribution to intake of total amount. RESULTS: Three-highest ranked food categories of total energy consumed were: rice (10·3%), yeast breads (6·9%), and turnovers and other grain-based items (6·8 %). Highest ranked food sources of total gram amount consumed were fruit drinks (9·6%), other 100% juice (9·3%) and rice (8·3%). Three highest ranked sources for added sugars were other 100% juice (24·1 %), fruit drinks (16·5%), and sugar and honey (12·4%). SFA ranked foods were turnovers and other grain-based (12·6 %), cheese (11·9%), and pizza (10·3%). Three top sources of Na were rice (13·9%), soups (9·1 %) and rice mixed dishes (7·3 %). CONCLUSION: Identification of top sources of energy and nutrients-to-limit among Latin Americans is critical for designing strategies to help them meet nutrient recommendations within energy needs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".