Assessing the Potential Adverse Consequences of Supplemental Calcium on Cardiovascular Outcomes: Should We Change Our Approach to Bone Health?
Bibliographic record
Abstract
OBJECTIVE: To assess cardiovascular risks associated with supplemental calcium use to assist clinicians with evidence-based recommendations for patients who have, or who are at risk for, osteoporosis or osteopenia. DATA SOURCES: Literature was accessed through December 2011 using MEDLINE, Cochrane Library, and International Pharmaceutical Abstracts using the terms calcium compounds and cardiovascular disease. In addition, reference citations from the publications identified were reviewed. STUDY SELECTION AND DATA EXTRACTION: All English-language articles were evaluated. Randomized controlled trials, observational studies, and meta-analyses were included. DATA SYNTHESIS: While supplemental calcium and vitamin D have been demonstrated to improve bone mineral density and decrease the risk of fractures, there have been recent reports that calcium supplements may increase the risk for cardiovascular events. Nine clinical trials and/or meta-analyses were reviewed; 3 documented increases in cardiovascular risk associated with calcium supplements, and 6 did not. No studies were designed to assess cardiovascular outcomes as primary end points. Balancing the evidence from these analyses with the results of randomized controlled trials assessing the effect of calcium on fracture prevention suggests that the benefits of calcium outweigh the cardiovascular risk. CONCLUSIONS: At this time, there is no cause to change routine practice surrounding supplemental calcium use in patients who have, or are at risk for, osteoporosis or osteopenia.
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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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".