Prevalence and Risk Factors of Osteoporosis in Women Referring to the Bone Densitometry Academic Center in Urmia, Iran
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is one of the fastest growing health problems around the world. Several factors can affect this silent disease. The current study aimed to determine the prevalence and risk factors of osteoporosis in women in Urmia, a city in northwestern Iran. METHODS: This cross‑sectional study was performed on 360 non-pregnant women over the age of 15 who referred for bone density testing to the Urmia Imam Khomeini Academic Hospital. Data were collected by questionnaire, and bone mineral density of the femoral neck and lumbar spines L1- L4 was evaluated by dual X-ray absorptiometry. RESULTS: The total prevalence of osteoporosis in this study was 42.2%; prevalence of osteoporosis among women 45 years old or less was 14.3% and over the age of 45 years was 50.7%. The factors such as level of education, history of bone fracture, disease history (rheumatoid arthritis, diabetes, high blood pressure), gravidity and parity values, duration of lactation (p<0.001), nutrition dimension of lifestyle (p=0.03), and green tea consumption (p=002) showed a statistically significant association with the bone mineral density. According to the regression model, age (OR=1.081), history of bone fracture (OR=2.75), and gravidity (OR=1.14) were identified as significant risk factors for osteoporosis, while the body mass index (OR=0.94) was identified as a protector against osteoporosis. CONCLUSION: The prevalence of osteoporosis in this study was high, and findings showed that the advancement of age, lifestyle, and reproductive factors (especially gravidity and duration of lactation) were determining factors for osteoporosis .Appropriate educational programs and interventions could help to increase the women's peak bone mass therefore reducing their risk of developing osteoporosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".