ArticlePeerJ2026
Application of a machine learning model based on routine clinical parameters for the diagnosis of rheumatoid arthritis with concomitant osteoporosis: a retrospective study.
Article in PeerJ, 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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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.
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4 authors.
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Abstract
Rheumatoid arthritis (RA) is commonly complicated by secondary osteoporosis (OP), affecting up to 80% of patients. Although dual-energy X-ray absorptiometry (DEXA) is the diagnostic gold standard, its limited accessibility highlights the need for alternative tools. In this retrospective cohort study of 396 hospitalized RA patients, we developed machine learning models using demographic and routine laboratory data to identify concomitant OP. Five classifiers were evaluated and combined
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