SynthesisBMC musculoskeletal disorders2026
Performance of clinical prediction models for identifying postmenopausal osteoporosis: a systematic review and meta-analysis.
Synthesis in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence, explainable deep learning, and generative models for osteoporosis and bone disease prediction: a systematic review and future research roadmap.Frontiers in artificial intelligence · 2026Pooled it
- 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
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeEarly clinical recognition of postmenopausal osteoporosis (PMOP) can be challenging. With the advancement of machine learning, several prediction models for PMOP have been developed. This study assessed their performance by carrying out a systematic review and meta-analysis.
methodsThe PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), WanFang database and China Science and Technology Journal Database (VIP) were systematically searched. Studies with a sample size of at least 100 and involving postmenopausal women were included Included models were descriptively summarized, and meta-analyses were conducted to derive discrimination estimates. Homogeneous results from different studies were pooled using MedCalc software.
resultsOut of 37,115 identified studies, 21 were included. Most of the models were developed using data from cross-sectional studies and the sample size of included models ranged from 103 to 12,175, totaling 45,383 participants with 16,008 positive events. Several models contained some similar predictors, including age, prior fractures, and body mass index (BMI). We also conducted a meta-analysis that included 22 models with reported AUC and its 95% confidence interval (95% CI), which demonstrated that the prediction models have good discriminative performance. The most frequently observed predictive variables include age, weight, body mass index (BMI), menopause status, height, fracture history, lower limb cramps, fatigue, waist circumference，diabetes mellitus，hyperlipidemia and glucocorticoids.
conclusionsWe found that the PMOP prediction models demonstrated promising performance. However, this review also highlights several potential limitations of current approaches, including a high risk of bias and limited external validation. Future research should aim to refine these models using larger and more diverse populations, as well as by incorporating additional risk factors to improve their clinical applicability.
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