Development and Evaluation of an Automated Algorithm to Estimate the Nutrient Intake of Infants from an Electronic Complementary Food Frequency Questionnaire
Bibliographic record
Abstract
Background: We previously validated a four-day complementary food frequency questionnaire (CFFQ) to estimate the nutrient intake in New Zealand infants aged 9-12 months. However, manual entry of the CFFQ data into nutritional analysis software was time-consuming. Therefore, we developed an automated algorithm and evaluated its accuracy by comparing the nutrient estimates with those obtained from the nutritional analysis software. Methods: We analysed 50 CFFQ completed at 9- and 12-months using Food Works nutritional analysis software. The automated algorithm was programmed in SAS by multiplying the average daily consumption of each food item by the nutrient content of the portion size. We considered the most common brands for commercially prepared baby foods. Intakes of energy, macronutrients, and micronutrients were compared between methods using Bland-Altman analysis. Results: The automated algorithm did not have any significant bias for estimates of energy (kJ) (MD 15, 95% CI -27, 58), carbohydrate (g) (MD -0.1, 95% CI -1.2,1.0), and fat (g) (-0.1, 95% CI -0.3,0.1), but slightly underestimated intake of protein (MD -0.4 g, 95% CI -0.7,-0.1), saturated fat, PUFA, dietary fibre, and niacin. The algorithm provided accurate estimates for other micronutrients. The limits of agreement were relatively narrow. Conclusion: This automated algorithm is an efficient tool to estimate the nutrient intakes from CFFQ accurately. The small negative bias observed for few nutrients was clinically insignificant and can be minimised. This algorithm is suitabl
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".