Limitations of Food Composition Databases and Nutrition Surveys for Evaluating Food Fortification in the United States and Canada
Bibliographic record
Abstract
Abstract Background As the availability of fortified foods expands, it is increasingly important to monitor risk of excessive nutrient intake. However, neither Canadian nor US nutrient composition databases systematically differentiate between naturally occurring nutrients and those added to foods at manufacturers’ discretion, and the consumption of fortified foods is not comprehensively assessed during dietary data collection. Objective To describe limitations in the estimation of nutrient intakes from voluntarily fortified foods from the Canadian Community Health Survey (CCHS 2004) and National Health and Nutrition Examination Survey (NHANES 2007-08) for the purposes of evaluating fortification policies and practices. Description Working with the US Food and Nutrient Database for Dietary Studies, we identified voluntarily fortified foods by food code descriptions containing certain key words and the presence of nutrients for which additions were tracked in the database. This strategy is likely to have resulted in an underestimation of voluntarily fortified food consumption and thus an underestimation of the probability of excessive intakes in the US population. Our efforts to model proposed policy changes to food fortification in Canada were similarly limited by our inability to differentiate added sources of niacin and retinol in the CCHS. This thwarted assessment of risks associated with fortification because the Tolerable Upper Intake Levels only apply to retinol and added niacin. Conclusion It is important that food composition databases and 24hr dietary recall collection methods evolve to facilitate monitoring and evaluating health benefits and risks associated with growing voluntary food fortification practices.
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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 | 0.042 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.027 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".