<i>Food Frequency Questionnaire</i>for Assessing Infant Iron Nutrition
Bibliographic record
Abstract
A food frequency questionnaire (FFQ) was developed and tested for assessing iron nutrition in infants through comparison with a three-day food record (3d-FR) and measures of iron status. Parents of 148 infants aged eight to 26 months completed a 3d-FR and an FFQ. Blood was collected for measures of hemoglobin (Hgb), ferritin, and transferrin receptor (sTfR). Iron deficiency anemia and iron depletion (ferritin < or =12 microg/L) were found in 9% and 26% of infants, respectively. The intakes of energy, total iron, heme and non-heme iron, vitamin C, and dietary fibre determined by the FFQ were associated with the intakes of the same nutrient determined by the 3d-FR (p<0.05). The intakes of energy, total iron, non-heme and heme iron, vitamin C, and fibre were significantly higher when estimated by the FFQ than by the 3d-FR. Total and heme iron intakes determined by the FFQ were significantly associated with serum ferritin, sTfR, and the sTfR:ferritin ratio (p<0.05). However, iron intakes explained <10% of the variability in iron status. Despite relative validity of the FFQ for evaluating differences in energy, iron, vitamin C, and fibre intake compared with a 3d-FR, FFQs need further development before they can be used to advance assessment of iron intake and status in infants.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".