Understanding Decision Making as It Influences Treatment in Thoracolumbar Burst Fractures Without Neurological Deficit: Conceptual Framework and Methodology

Study Design This paper presents a description of a conceptual framework and methodology that is applicable to the manuscripts that comprise this focus issue. Objectives Our goal is to present a conceptual framework which is relied upon to better understand the processes through which surgeons make...

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Veröffentlicht in:Global spine journal 2024-02, Vol.14 (1_suppl), p.8S-16S
Hauptverfasser: Dandurand, Charlotte, Öner, Cumhur F., Hazenbiller, Olesja, Bransford, Richard J., Schnake, Klaus, Vaccaro, Alexander R., Benneker, Lorin M., Vialle, Emiliano, Schroeder, Gregory D., Rajasekaran, Shanmuganathan, El-Skarkawi, Mohammad, Kanna, Rishi M., Aly, Mohamed, Holas, Martin, Canseco, Jose A., Muijs, Sander, Popescu, Eugen Cezar, Tee, Jin Wee, Camino-Willhuber, Gaston, Joaquim, Andrei Fernandes, Keynan, Ory, Chhabra, Harvinder Singh, Bigdon, Sebastian, Spiegel, Ulrich, Dvorak, Marcel F.
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Sprache:eng
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Zusammenfassung:Study Design This paper presents a description of a conceptual framework and methodology that is applicable to the manuscripts that comprise this focus issue. Objectives Our goal is to present a conceptual framework which is relied upon to better understand the processes through which surgeons make therapeutic decisions around how to treat thoracolumbar burst fractures (TL) fractures. Methods We will describe the methodology used in the AO Spine TL A3/4 Study prospective observational study and how the radiographs collected for this study were utilized to study the relationships between various variables that factor into surgeon decision making. Results With 22 expert spine trauma surgeons analyzing the acute CT scans of 183 patients with TL fractures we were able to perform pairwise analyses, look at reliability and correlations between responses and develop frequency tables, and regression models to assess the relationships and interactions between variables. We also used machine learning to develop decision trees. Conclusions This paper outlines the overall methodological elements that are common to the subsequent papers in this focus issue.
ISSN:2192-5682
2192-5690
DOI:10.1177/21925682231210183