An Insight into the Effect of Exercises on the Prevention of Osteoporosis and Associated Fractures in High-risk Individuals
Bibliographic record
Abstract
The purpose of this review was to investigate what type of exercises can potentially prevent osteoporosis (OP) and its associated fractures in high-risk populations. MEDLINE was searched for work relevant to various types of exercises used to prevent osteoporotic fractures in high-risk population, from the year 1995 onwards. Twelve articles were identified, and, from them, four were deemed suitable to the objective. The studies reviewed show that various types of exercise are effective and safe in preventing the onset of OP. For example, high-intensity progressive resistance training (HiPRT) has been shown to increase vertebral height and femoral neck bone mineral density (BMD), in addition to improving functional performance. Additional studies reviewed suggested that bone reabsorption levels may be positively impacted by low-impact exercise, such as walking. This review provides insight into the effectiveness of various types of exercise to combat and possibly prevent OP for high-risk individuals, which include postmenstrual Caucasian females, people with multiple comorbidities, individuals who smoke or consume alcohol, and the frail elderly population. The prevention of OP should reduce both the social (emotional) and economic burdens faced by patients, caregivers, and health-care systems. Moving forward, research that identifies and bridges pharmaceutical treatment and exercise should be conducted, in addition to the comparison of passive versus active forms of exercise to determine which treatment best prevents OP in high-risk populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".