Understanding Decision Making as It Influences Treatment in Thoracolumbar Burst Fractures Without Neurological Deficit: Conceptual Framework and Methodology.
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BORIS DOI
Publisher DOI
PubMed ID
38324598
Description
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.
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.
Date of Publication
2024-02
Publication Type
Article
Subject(s)
Keyword(s)
burst fractures methodology thoracolumbar treatment recommendations
Language(s)
en
Contributor(s)
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 | |
Spiegel, Ulrich | |
Dvorak, Marcel F |
Additional Credits
Series
Global spine journal
Publisher
Sage
ISSN
2192-5682
Access(Rights)
open.access