Magnesium Intake Predicts Bone Turnover in Postmenopausal Black South African Women
Bibliographic record
Abstract
This prospective study investigated the association between nutrient intake, dietary patterns, and changes in bone turnover and bone mineral density (BMD) in postmenopausal urban black South African women over two years. These women (n = 144) underwent BMD measurements at the distal radius, lumbar spine, femoral neck (FN), as well as a biochemical analysis which included the parathyroid hormone (PTH), 25-hydroxyvitamin D, C-Telopeptide of type I collagen (CTX-1) in 2010 and 2012. Their dietary intake was assessed in 2010 using a food frequency questionnaire, and sociodemographic and health information was collected. Four dietary patterns explained 54.4% of the variance of dietary intake, namely staple foods and processed meats, home cooking, snacking, and high sugar. Dietary magnesium negatively correlated with CTx-1 in 2012 (r = −0.21, p = 0.02), calcium correlated with distal radius BMD in 2010 (r = 0.22, p = 0.01) and 2012 (r = 0.24, p = 0.005), and the snacking dietary pattern score correlated with FN BMD in 2010 (r = 0.18, p = 0.03) and 2012 (r = 0.21, p = 0.02). The baseline CTx-1 and dietary magnesium intake predicted 22% of the variance in percentage change of CTx-1 over two years (p < 0.001).The magnesium intake predicted short-term bone resorption over two years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".