Randomized controlled trial comparing the bioavailability of vitamin D <sub>3</sub> from fortified Cheddar cheese, fortified low‐fat cheese, and supplement
Bibliographic record
Abstract
Objective: We compared the bioavailability of vitamin D3 from novel fortified hard cheeses and from vitamin D3 supplement. Methods: 60 adults were randomly given weekly servings of fortified Cheddar cheese (34 g), fortified low‐fat cheese (41 g), or vitamin D 3 supplement. These contained 28,000 IU vitamin D 3 , equivalent to 4,000 IU/d. 20 adults were also randomized to placebo. Bioavailability of vitamin D 3 was assessed by comparing the serum 25‐hydroxyvitamin D [25(OH)D] response over 8 weeks. Results: In the placebo group, initial 25(OH)D, 55.0 ± 25.3 nmol/L (mean ± SD), declined over the 8‐wk winter protocol, to 50.7 ± 24.2 nmol/L (paired t test, p=0.046). In the vitamin D 3 ‐treated groups, the mean increases in 25(OH)D from baseline to 8 wk were: 65.3 ± 24.1 nmol/L (Cheddar cheese), 69.4 ± 21.7 (low‐fat cheese), 59.3 ± 23.3 (supplement with food), and 59.3 ± 19.6 (supplement without food); these changes were significantly different from the placebo group (ANOVA, p<0.0001) but not from one another (ANOVA, p=0.62). Compared with baseline, serum parathyroid hormone decreased significantly with both the fortification (paired t test, p=0.003) and the supplementation (paired t test, p=0.012) strategies. Conclusions: Vitamin D 3 is equally bioavailable from fortified hard cheeses and from supplement. Therefore, hard cheese is a viable food for vitamin D fortification, and can help to enhance vitamin D status.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".