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Study Design: Cross-sectional survey., Objectives: Injury classifications are important tools to identify fracture patterns, guide treatment-decisions and aid to identify optimal treatment plans. The AO Spine-DGOU Osteoporotic Fracture (OF) classification system was developed, and the aim of this study was to assess the reliability of this new classification system., Methods: 23 Members of the AO Spine Knowledge Forum Trauma participated in the validation process. Participants were asked to rate 33 cases according to the OF classification at 2 time points, 4 weeks apart (assessment 1 and 2). The kappa statistic (κ) was calculated to assess inter-observer reliability and intra-rater reproducibility. The gold master key for each case was determined by approval of at least 5 out of 7 members of the DGOU., Results: A total of 1386 ratings (21 raters) were performed. The overall inter-rater agreement was moderate with a combined kappa statistic for the OF classification of 0.496 in assessment 1 and 0.482 in assessment 2. The combined percentage of correct ratings (compared to gold-standard) in assessment 1 was 71.4% and 67.4% in assessment 2. The average intra-rater reproducibility was substantial (κ = 0.74, median 0.76, range 0.55 to 1.00, SD 0.13) for the assessed fracture types., Conclusions: The assessed overall inter-rater reliability was moderate and substantial in some instances. The average intra-rater reproducibility is substantial. It seems that appropriate training of the classification system can enhance inter- and intra-rater reliability., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The AO Spine-DGOU Injury Classification System was developed and funded by AO Spine through the AO Spine Knowledge Forum Trauma, a focused group of international spine trauma experts. AO Spine is a clinical division of the AO Foundation, which is an independent medically-guided not-for-profit organization. Individual authors of this manuscript have no disclosures to declare regarding the proposed article.

Study Design: Predictive algorithm via decision tree., Objectives: Artificial intelligence (AI) remain an emerging field and have not previously been used to guide therapeutic decision making in thoracolumbar burst fractures. Building such models may reduce the variability in treatment recommendations. The goal of this study was to build a mathematical prediction rule based upon radiographic variables to guide treatment decisions., Methods: Twenty-two surgeons from the AO Knowledge Forum Trauma reviewed 183 cases from the Spine TL A3/A4 prospective study (classification, degree of certainty of posterior ligamentous complex (PLC) injury, use of M1 modifier, degree of comminution, treatment recommendation). Reviewers' regions were classified as Europe, North/South America and Asia. Classification and regression trees were used to create models that would predict the treatment recommendation based upon radiographic variables. We applied the decision tree model which accounts for the possibility of non-normal distributions of data. Cross-validation technique as used to validate the multivariable analyses., Results: The accuracy of the model was excellent at 82.4%. Variables included in the algorithm were certainty of PLC injury (%), degree of comminution (%), the use of M1 modifier and geographical regions. The algorithm showed that if a patient has a certainty of PLC injury over 57.5%, then there is a 97.0% chance of receiving surgery. If certainty of PLC injury was low and comminution was above 37.5%, a patient had 74.2% chance of receiving surgery in Europe and Asia vs 22.7% chance in North/South America. Throughout the algorithm, the use of the M1 modifier increased the probability of receiving surgery by 21.4% on average., Conclusion: This study presents a predictive analytic algorithm to guide decision-making in the treatment of thoracolumbar burst fractures without neurological deficits. PLC injury assessment over 57.5% was highly predictive of receiving surgery (97.0%). A high degree of comminution resulted in a higher chance of receiving surgery in Europe or Asia vs North/South America. Future studies could include clinical and other variables to enhance predictive ability or use machine learning for outcomes prediction in thoracolumbar burst fractures., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Study Design: Reliability study utilizing 183 injury CT scans by 22 spine trauma experts with assessment of radiographic features, classification of injuries and treatment recommendations., Objectives: To assess the reliability of the AOSpine TL Injury Classification System (TLICS) including the categories within the classification and the M1 modifier., Methods: Kappa and Intraclass correlation coefficients were produced. Associations of various imaging characteristics (comminution, PLC status) and treatment recommendations were analyzed through regression analysis. Multivariable logistic regression modeling was used for making predictive algorithms., Results: Reliability of the AO Spine TLICS at differentiating A3 and A4 injuries (N = 71) (K = .466; 95% CI .458 - .474; P < .001) demonstrated moderate agreement. Similarly, the average intraclass correlation coefficient (ICC) amongst A3 and A4 injuries was excellent (ICC = .934; 95% CI .919 - .947; P < .001) and the ICC between individual measures was moderate (ICC = .403; 95% CI .351 - .461; P < .001). The overall agreement on the utilization of the M1 modifier amongst A3 and A4 injuries was fair (K = .161; 95% CI .151 - .171; P < .001). The ICC for PLC status in A3 and A4 injuries averaged across all measures was excellent (ICC = .936; 95% CI .922 - .949; P < .001). The M1 modifier suggests respondents are nearly 40% more confident that the PLC is injured amongst all injuries. The M1 modifier was employed at a higher frequency as injuries were classified higher in the classification system., Conclusions: The reliability of surgeons differentiating between A3 and A4 injuries in the AOSpine TLICS is substantial and the utilization of the M1 modifier occurs more frequently with higher grades in the system., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Study Design: A prospective study., Objective: to evaluate the impact of vertebral body comminution and Posterior Ligamentous Complex (PLC) integrity on the treatment recommendations of thoracolumbar fractures among an expert panel of 22 spine surgeons., Methods: A review of 183 prospectively collected thoracolumbar burst fracture computed tomography (CT) scans by an expert panel of 22 trauma spine surgeons to assess vertebral body comminution and PLC integrity. This study is a sub-study of a prospective observational study of thoracolumbar burst fractures (Spine TL A3/A4). Each expert was asked to grade the degree of comminution and certainty about the PLC disruption from 0 to 100, with 0 representing the intact vertebral body or intact PLC and 100 representing complete comminution or complete PLC disruption, respectively., Results: ≥45% comminution had a 74% chance of having surgery recommended, while <25% comminution had an 86.3% chance of non-surgical treatment. A comminution from 25 to 45% had a 57% chance of non-surgical management. ≥55% PLC injury certainity had a 97% chance of having surgery, and ≥45-55% PLC injury certainty had a 65%. <20% PLC injury had a 64% chance of having non-operative treatment. A 20 to 45% PLC injury certainity had a 56% chance of non-surgical management. There was fair inter-rater agreement on the degree of comminution (ICC .57 [95% CI 0.52-.63]) and the PLC integrity (ICC .42 [95% CI 0.37-.48])., Conclusion: The study concludes that vetebral comminution and PLC integrity are major dterminant in decision making of thoracolumbar fractures without neurological deficit. However, more objective, reliable, and accurate methods of assessment of these variables are warranted., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The aim of this study was to determine whether early surgical treatment results in better neurological recovery 12 months after injury than late surgical treatment in patients with acute traumatic spinal cord injury (tSCI). Patients with tSCI requiring surgical spinal decompression presenting to 17 centres in Europe were recruited. Depending on the timing of decompression, patients were divided into early (≤ 12 hours after injury) and late (> 12 hours and < 14 days after injury) groups. The American Spinal Injury Association neurological (ASIA) examination was performed at baseline (after injury but before decompression) and at 12 months. The primary endpoint was the change in Lower Extremity Motor Score (LEMS) from baseline to 12 months. The final analyses comprised 159 patients in the early and 135 in the late group. Patients in the early group had significantly more severe neurological impairment before surgical treatment. For unadjusted complete-case analysis, mean change in LEMS was 15.6 (95% confidence interval (CI) 12.1 to 19.0) in the early and 11.3 (95% CI 8.3 to 14.3) in the late group, with a mean between-group difference of 4.3 (95% CI -0.3 to 8.8). Using multiply imputed data adjusting for baseline LEMS, baseline ASIA Impairment Scale (AIS), and propensity score, the mean between-group difference in the change in LEMS decreased to 2.2 (95% CI -1.5 to 5.9). Compared to late surgical decompression, early surgical decompression following acute tSCI did not result in statistically significant or clinically meaningful neurological improvements 12 months after injury. These results, however, do not impact the well-established need for acute, non-surgical tSCI management. This is the first study to highlight that a combination of baseline imbalances, ceiling effects, and loss to follow-up rates may yield an overestimate of the effect of early surgical decompression in unadjusted analyses, which underpins the importance of adjusted statistical analyses in acute tSCI research., Competing Interests: The authors have no conflicts of interest to declare., (© 2023 Author(s) et al.)