Calcium and vitamin D for increasing bone mineral density in premenopausal women
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is a condition where bones become fragile due to low bone density and impaired bone quality. This results in fractures that lead to higher morbidity and reduced quality of life. Osteoporosis is considered a major public health concern worldwide. For this reason, preventive measurements need to be addressed throughout the life course. Exercise and a healthy diet are among the lifestyle factors that can help prevent the disease, the latter including intake of key micronutrients for bone, such as calcium and vitamin D. The evidence on whether supplementation with calcium and vitamin D improves bone mineral density (BMD) in premenopausal women is still inconclusive. In this age group, bone accrual is considered to be the goal of supplementation, so BMD is relevant for the future stages of life. OBJECTIVES: To evaluate the benefits and harms of calcium and vitamin D supplementation, alone or in combination, to increase the BMD, reduce fractures, and report the potential adverse events in healthy premenopausal women compared to placebo. SEARCH METHODS: We used standard, extensive Cochrane search methods. The latest search was 12 April 2022. SELECTION CRITERIA: ). DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. Outcomes included total hip bone mineral density (BMD), lumbar spine BMD, quality of life, new symptomatic vertebral fractures, new symptomatic non-vertebral fractures, withdrawals due to adverse events, serious adverse events, all reported adverse events and additional withdrawals for any reason. MAIN RESULTS: = 72%; 2 studies, 541 participants; low-certainty evidence). AUTHORS' CONCLUSIONS: Our results do not support the isolated or combined use of calcium and vitamin D supplementation in healthy premenopausal women as a public health intervention to improve BMD in the total hip or lumbar spine, and therefore it is unlikely to have a benefit for the prevention of fractures (vertebral and non-vertebral). The evidence found suggests that there is no need for future studies in the general population of premenopausal women; however, studies focused on populations with a predisposition to diseases related to bone metabolism, or with low bone mass or osteoporosis diagnosed BMD would be useful.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".