Osteoporosis Prevention, Screening, and Treatment: A Review
Bibliographic record
Abstract
Osteoporosis, defined as low bone mass leading to increased fracture risk, is a major health problem that affects approximately 10 million Americans. The aging U.S. population is predicted to contribute to as much as a 50% increase in prevalence by 2025. Although common, osteoporosis can be clinically silent, and without prevention and screening, the costs of osteoporotic fracture-related morbidity and mortality will burden the U.S. healthcare system. This is a particularly relevant concern in the context of diminishing health care resources. Dual-energy X-ray absorptiometry is the most widely used, validated technique for measuring bone mineral density (BMD) and diagnosing osteoporosis. Cost-effectiveness analyses support early detection and treatment of high-risk patients with antiresorptive medications such as bisphosphonates. Moreover, optimization of bone health throughout life can help prevent osteoporosis. Current guidelines recommend screening women by age 65 years, but because no guidelines for screening intervals exist, decisions are made on the basis of clinical judgment alone. Although the recent literature provides some guidance, this review further explores current recommendations in light of newer evidence to provide more clarity on prevention, screening, and management strategies for patients with osteoporosis in the primary care setting.
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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) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".