ArticleGlobal spine journal2024
Understanding Decision Making as It Influences Treatment in Thoracolumbar Burst Fractures Without Neurological Deficit: Conceptual Framework and Methodology.
Article in Global spine journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- The AO Spine Knowledge Forums: A Decade of Impactful Spine Research.Global spine journal · 2025Article
- [Current surgical treatment concepts for traumatic thoracic and lumbar vertebral fractures in adults with good bone quality].Unfallchirurgie (Heidelberg, Germany) · 2025Review
- Predictive accuracy of machine learning models for conservative treatment failure in thoracolumbar burst fractures.BMC musculoskeletal disorders · 2024Article
- Summary and Future Strategies in Anticipation of the A3 A4 Fracture Study Results.Global spine journal · 2024Article
Corrections and comments
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Authors and funding
25 authors at 17 institutions in 13 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designThis paper presents a description of a conceptual framework and methodology that is applicable to the manuscripts that comprise this focus issue.
objectivesOur 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.
methodsWe 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.
resultsWith 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.
conclusionsThis paper outlines the overall methodological elements that are common to the subsequent papers in this focus issue.
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Registered trials
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