Risk Factors for the Development of Osteoporosis and Osteoporotic Fractures Among Older Men
Bibliographic record
Abstract
OBJECTIVE: Osteoporotic fractures are associated with significant morbidity and mortality particularly among older men. However, there is little information regarding risk factors among this population. The aims of our study were to determine risk factors for osteoporosis and fragility fractures and the predictive value of bone mineral density (BMD) measurements for development of fragility fractures in a cohort of elderly Caucasian and African American men. METHODS: We evaluated 257 men aged 70 years or older for risk factors for osteoporosis and fragility fractures using a detailed questionnaire and BMD assessment. Exclusion criteria included conditions known to cause osteoporosis such as hypogonadism and chronic steroid use, current treatment with bisphosphonates, bilateral hip arthroplasties, and inability to ambulate independently. RESULTS: Age, weight, weight loss, androgen deprivation treatment, duration of use of dairy products, exercise, and fracture within 10 years prior to study entry were associated with osteoporosis (p < or = 0.05). Fragility fractures were associated with duration of use of dairy products, androgen deprivation treatment, osteoporosis, and history of fracture within 10 years prior to BMD assessment (p < or = 0.05). There were some differences in risk factors between the Caucasian and African American populations, suggesting that risk factors may vary between ethnic groups. CONCLUSION: Although men with osteoporosis had a higher rate of fractures, the majority of fractures occurred in men with T-scores > -2.5 standard deviations below the mean, suggesting that factors other than BMD are also important in determining risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".