The Role of Exercises in Osteoporotic Fracture Prevention and Current Care Gaps. Where Are We Now? Recent Updates
Bibliographic record
Abstract
INTRODUCTION: The primary non-pharmacological management recommended for patients with osteoporosis (OP) is exercise, but whether it should be high-force, resistive, or other means can be obscure. OBJECTIVE: To describe the role of exercises in osteoporotic fracture prevention, identify effects and potential risks of high-force exercises, detect the optimal exercises to combat OP, and explore the challenges that might arise from interventions. METHODS: A search on MEDLINE and Cochrane databases was conducted on the role of exercises in preventing osteoporotic fractures from 1989 onwards, leading to 40 results, including op-ed pieces, qualitative studies, randomized clinical trials (RCTs) (n=5), and RCT follow-up studies (n=1). Articles deemed relevant to the objectives were analyzed and summarized. Data on effects of vitamin D and calcium supplementation were later gathered from different sources as well. RESULTS: High-intensity, resistive strength training provided the maximum benefit in increasing bone mineral density (BMD) levels, muscle mass, and reduction in fractures, while posture and balance exercises only improved mobility. High-force exercises did not increase fractures and were associated with increases in BMD. Interventions including exercises, vitamin D, and calcium intake had limited effect when used as single interventions, while vitamin D and calcium may potentially cause increases of cardiovascular events. CONCLUSION: A long-term regular exercise program designed to improve postural stability, mobility, and mechanical efficiency, alongside an increased vitamin D and dietary calcium intake, is most effective in preventing OP and reducing osteoporotic fractures.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".