<i>Protein and Bone Health:</i>Literature Review and Counselling Implications
Bibliographic record
Abstract
For decades, public health promotion campaigns on bone health have emphasized the importance of adequate calcium and vitamin D intakes, as well as weight-bearing physical activity. However, no obvious consensus has emerged on the role of dietary protein. To identify what agreement does exist in the literature, in this article we review the theoretical basis for protein's role in bone health, assess some recent cross-sectional and prospective studies, and generate recommendations for practice. There is general agreement in the literature that higher protein intake increases urinary calcium loss; the body compensates for this loss by increasing calcium absorption in the gut, providing that calcium intake is sufficient. A possible explanation for calcium loss, the "acid-ash" hypothesis, is discussed, and suggestions are made about food choices that may counter the calciuric effect of protein. A survey of cross-sectional and prospective studies shows equivocal results, with confounding variables complicating the analysis. Both deficient and excessive protein intakes have been shown to affect bone health negatively, although lower and upper thresholds have not been determined. Practical advice on achieving bone health is given, with an emphasis on the use of Canada's Food Guide to Healthy Eating in setting dietary goals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".