ArticleJBMR plus2019
Can Classification and Regression Tree Analysis Help Identify Clinically Meaningful Risk Groups for Hip Fracture Prediction in Older American Men (The MrOS Cohort Study)?
Article in JBMR plus, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
14 citing papers in PubMed, 21 citations in OpenAlex.
- An explainable machine learning approach to predict fragility fractures and the identification of important features.Scientific reports · 2026Article
- Machine learning prediction for menopause women with low bone mass: a multicenter and retrospective study.Scientific reports · 2026Article
- Prophylactic fixation in elderly fractures: Preventive breakthrough or unnecessary intervention?World journal of orthopedics · 2025Review
- Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.PLOS digital health · 2024Article
- AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review.Journal of orthopaedic surgery and research · 2023Review
- Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old.JBMR plus · 2023Article
- Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm.Endocrinology and metabolism (Seoul, Korea) · 2022Article
- Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal.Aging and disease · 2022Review
- Reverse engineering the FRAX algorithm: Clinical insights and systematic analysis of fracture risk.Bone · 2022Article
- Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis.Therapeutic advances in musculoskeletal disease · 2022Article
- Application of Machine Learning to Identify Clinically Meaningful Risk Group for Osteoporosis in Individuals Under the Recommended Age for Dual-Energy X-Ray Absorptiometry.Calcified tissue international · 2021Article
- Applications of Machine Learning in Bone and Mineral Research.Endocrinology and metabolism (Seoul, Korea) · 2021Review
- Development and validation of the fracture risk scale home care (FRS-HC) that predicts one-year incident fracture: an electronic record-linked longitudinal cohort study.BMC musculoskeletal disorders · 2020Article
- Machine Learning Applications in Endocrinology and Metabolism Research: An Overview.Endocrinology and metabolism (Seoul, Korea) · 2020Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
Abstract
Although the WHO fracture risk algorithm (FRAX) is used to predict fracture, the utility of some simple machine-learning methods, such as classification and regression trees (CARTs) should be evaluated to determine their efficacy in fracture prediction. Follow-up time for the hip fracture analyses of 5977 community-dwelling American men aged ≥65 years old was truncated to 10 years. There were 172 (2.9%) men who had an incident nontraumatic hip fracture. The CARTs were developed using hip BMD and common clinical risk factors as follows: model 1 = using classification with continuous variables of age, total hip BMD, and femoral neck BMD, or together with common clinical risk factors; and model 2 = using classification with continuous variables of age, total hip BMD, femoral neck BMD, FRAX score, osteoporosis by
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.