Food fortification for bone health in adulthood: a scoping review
Bibliographic record
Abstract
Food fortification can deliver essential micronutrients to large population segments without modifications in consumption pattern, suggesting that fortified foods may be formulated for populations at risk for fragility fractures. This scoping review determined the extent to which randomized controlled studies have been carried out to test the impact of fortified foods on bone outcomes, searching PubMed for all studies using the terms 'fortified AND bone', and 'fortification AND bone'. Studies were restricted to English language, published between 1996 and June 2015. From 360 articles, 24 studies met the following criteria: human study in adults ⩾18 years (excluding pregnancy or lactation); original study of a fortified food over time, with specific bone outcomes measured pre- and post intervention. Six studies involved adults <50 years; 18 involved adults ⩾50 years. Singly or in combination, 17 studies included calcium and 16 included vitamin D. There were 1 or 2 studies involving either vitamin K, magnesium, iron, zinc, B-vitamins, inulin or isoflavones. For adults <50 years, the four studies involving calcium or vitamin D showed a beneficial effect on bone remodeling. For adults ⩾50 years, n=14 provided calcium and/or vitamin D, and there was a significant bone turnover reduction. No consistent effects were reported in studies in which addition of vitamin K, folic acid or isoflavone was assessed. Results from this scoping review indicate that up to now most studies of fortification with bone health have evaluated calcium and/or vitamin D and that these nutrients show beneficial effects on bone remodeling.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".