ArticleClinical interventions in aging2023
Validation of Three Tools for Estimating the Risk of Primary Osteoporosis in an Elderly Male Population in Beijing.
Article in Clinical interventions in aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Explainable machine learning for osteoporosis detection in patients with osteopenia: model development and validation using routine clinical data from an Asian cohort.Frontiers in endocrinology · 2026Article
- Performance of USPSTF-recommended osteoporosis risk assessment tools in identifying osteoporosis in older men: a multicentre retrospective study.Frontiers in endocrinology · 2025Article
- Construction of a predictive model for osteoporosis risk in men: using the IOF 1-min osteoporosis test.Journal of orthopaedic surgery and research · 2023Article
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8 authors.
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Abstract
Purpose: This cross-sectional study estimated three clinical tools including the Osteoporosis Self-Assessment Tool for Asians (OSTA), Body Mass Index (BMI), and Beijing Friendship Hospital Osteoporosis Self-assessment Tool for Elderly Male (BFH-OSTM) for identifying primary osteoporosis and found optimal cut-off values in an elderly Han Beijing male population. Materials and Methods: We conducted a cross-sectional study, enrolling 400 community-dwelling elderly Han Beijing males aged ≥50 from 8 medical institutions. Osteoporosis was diagnosed as a T-score of -2.5 standard deviations or lower than that of the average young adult in different diagnostic criteria [lumbar spine (L1-L4), femoral neck, total hip, WHO]. BFH-OSTM, OSTA, and BMI were assessed for predicting OP by receiver operating characteristic (ROC) curves. Sensitivity, specificity, and areas under the ROC curves (AUC) were determined. Ideal thresholds for the omission of screening BMD were proposed. Results: The prevalence of osteoporosis ranged from 9.25% to 19.0% according to different diagnostic criteria. The present study indicated the highest discriminating ability was BFH-OSTM in different criteria. The AUCs of OSTA and BMI were 0.748 and 0.770 in WHO criteria, which suggested limiting predictive value for identifying OP in elderly Beijing males. The AUC of BFH-OSTM to predict OP based on WHO criteria was 0.827, yielding a sensitivity of 65.8% and specificity of 82.7%, respectively. With a cost of missing 6.5% of osteoporosis patients, BFH-OSTM could reduce 73.5% of participants in screening BMD tests. Conclusion: BFH-OSTM may be a simple and effective tool for identifying OP in the elderly male population in Beijing to omit BMD screening reasonably.
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