ArticleSAGE open medicine2023
Clinical prediction rule for bacterial arthritis: Chi-squared automatic interaction detector decision tree analysis model.
Article in SAGE open medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Differences in demographic factors, symptoms, and laboratory data between bacterial and non-bacterial arthritis have not been defined. We aimed to identify predictors of bacterial arthritis, excluding synovial testing. Methods: This retrospective cross-sectional survey was performed at a university hospital. All patients included received arthrocentesis from January 1, 2010, to December 31, 2020. Clinical information was gathered from medical charts from the time of synovial fluid sample collection. Factors potentially predictive of bacterial arthritis were analyzed using the Student's Results: A total of 460 patients (male/female = 229/231; mean ± standard deviation age, 70.26 ± 17.66 years) were included, of whom 68 patients (14.8%) had bacterial arthritis. The chi-squared automatic interaction detector decision tree analysis revealed that patients with C-reactive protein > 21.09 mg/dL (incidence of septic arthritis: 48.7%) and C-reactive protein ⩽ 21.09 mg/dL plus 27.70 < platelet count ⩽ 30.70 × 10 Conclusions: Our results emphasize that patients categorized as high risk of bacterial arthritis, and appropriate treatment could be initiated as soon as possible.
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