Development of a Database for the Estimation of Heme Iron and Nonheme Iron Content of Animal-Based Foods
Bibliographic record
Abstract
Background: Total iron (TI) intake and differentiation between heme iron (HI) and nonheme iron (NHI) are uncommon despite markedly different bioavailability. Objectives: To create a database compiling information from studies that directly assessed the HI content of animal products using the Hornsey method, and to explore differences in estimates of HI intake between the data compiled and the Monsen method. Methods: A literature search identified studies that chemically characterized the HI content of animal-based foods using the Hornsey method; HI, NHI, and TI contents (mg/100 g) were compiled. Information was grouped by animal type and cooking method, and mean (± SD) HI% was calculated. Using a 24-h dietary record, differences in HI and NHI intake using the compiled information and the Monsen approach were explored. Results: Actual HI% values ranged from 7% to 94%. Raw foods had the highest HI% [raw duck (94% ± 4%), raw blood curd (82% ± 4%), and raw beef (79% ± 9%)]. Boiled foods had the lowest HI% [boiled shrimp (11% ± 5%) and meatballs (15% ± 6%)]. Cooked foods with the highest HI% were beef (70% ± 10%) and lamb (70% ± 9%). In many instances, applying actual HI% from the complied database produced markedly different measures of the HI content of foods [cooked beef (Monsen: 1.3 mg/100 g); (Hornsey: 2.3 mg/100 g)]. Estimation of iron intake in a 24-h recall demonstrated that using animal-specific HI% results in different estimates of HI intake [Monsen: 1.2 mg HI (40%); Hornsey: 1.8 mg HI (59%)]. Conclusions: Animal-based foods have variable HI%. A fixed HI:NHI ratio does not reflect this variation and could give rise to inaccurate estimates of HI content in food and HI intake. Consideration of this variation in HI% may improve our ability to link dietary intake with iron status and important health outcomes.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".