Cardiovascular disease‐risk factors in middle‐aged osteopaenic women treated with calcium alone or combined to three nutrients essential to artery and bone collagen
Bibliographic record
Abstract
BACKGROUND: Recent research suggests that cardiovascular disease (CVD) and bone loss are functionally interwoven. This study examined the concomitant effects of a nutritional treatment of osteopaenia on CVD-risk factors. METHODS: A 1-year placebo-controlled trial was conducted on middle-aged women with normal (group A) or low (groups B and C) bone mineral density. Subjects (n = 20 per group) took daily either a placebo, calcium carbonate alone or combined to a vitamin (C and B(6))-proline capsule, respectively. Urinary pyridoxic acid (used to assess treatment compliance), plasma homocysteine, serum lipids and lipoproteins were measured before and after nutritional intervention. RESULTS: Groups were comparable at baseline in most parameters of interest. No changes occurred in groups A and B. The 4%, 7% and 25% reductions of total cholesterol, LDL and triglycerides, and 14% elevation of HDL were all significant in group C. A trend toward reduction was observed for homocysteine in this group. CONCLUSIONS: Vitamins C (500 mg) and B(6) (75 mg) combined with proline had consistent beneficial effects on CVD-risk factors, whereas calcium alone did not. This study also underlined the importance of considering vitamin B(6) status as a potential CVD risk factor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".