A practical model for identification of children at risk of excess energy intake in the developing world
Bibliographic record
Abstract
OBJECTIVE: We describe diet quality by demographic factors and weight status among Barbadian children and examine associations with excess energy intake (EI). A screening tool for the identification of children at risk of excess EI was developed. DESIGN: In a cross-sectional survey, the Diet Quality Index-International (DQI-I) was used to assess dietary intakes from repeat 24h recalls among 362 children aged 9-10 years. Participants were selected by probability proportional to size. A model to identify excess energy intake from easily measured components of the DQI-I was developed. SETTING: Barbados.ParticipantsPrimary-school children in Barbados. RESULTS: Over one-third of children were overweight/obese, and mean EI for boys (8644 (se 174·5) kJ/d (2066 (se 41·7) kcal/d)) and girls (8912 (se 169·9) kJ/d (2130 (se 40·6) kcal/d)) exceeded the RDA. Children consuming a variety of food groups, more vegetables and fruits, and lower percentage energy contribution from empty-calorie foods showed reduced likelihood of excess EI. Intake of more than 2400 mg Na/d and higher macronutrient and fatty acid ratios were positively related to the consumption of excess energy. A model using five DQI-I components (overall food group variety, variety for protein source, vegetables, fruits and empty calorie intake) had high sensitivity for identification of children at risk of excess EI. CONCLUSIONS: Children's diet quality, despite low intakes of fruit and vegetables, was within acceptable ranges as assessed by the DQI-I and RDA; however, portion size was large and EI high. A practical model for identification of children at risk of excess EI has been developed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".