ArticleFrontiers in medicine2026
Predicting poor response to anti-osteoporosis therapy: a machine learning model integrating clinical and novel biomarker data.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study was conducted to develop and validate a prediction model integrating clinical characteristics and novel biomarkers. The goal was to identify patients at high risk for a poor response to standard anti-osteoporosis therapy prior to treatment initiation, thereby supporting personalized therapeutic decision-making. Methods: A retrospective analysis was performed on 543 patients with primary osteoporosis admitted between January 2021 and December 2024. All patients received 12 months of standard treatment. Participants were randomly allocated to a training set ( Results: Baseline characteristics were comparable between the training and validation sets ( Conclusion: A predictive model integrating multi-dimensional factors was successfully developed and validated for assessing osteoporosis treatment efficacy. The RF-based model exhibited robust predictive performance and clinical utility. It shows potential for pre-therapeutic identification of high-risk patients, facilitating precision management in osteoporosis.
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