Establishing the Injury Severity of Thoracolumbar Trauma: Confirmation of the Hierarchical Structure of the AOSpine Thoracolumbar Spine Injury Classification System
Bibliographic record
Abstract
Introduction The AOSpine Thoracolumbar Spine Injury Classification System was recently published and combines elements of both the Magerl system and the Thoracolumbar Injury Classification System (TLICS); however, the injury severity of each fracture has yet to be established. The objective of this study is to develop a validated regional and global injury severity scoring system for thoracolumbar trauma. Material and Methods A survey was sent to 100 AOSpine members from all six AO regions of the world (North America, South America, Europe, Africa, Asia, and the Middle East). Each respondent was asked to numerically grade the severity of each variable of the AOSpine Thoracolumbar Spine Injury Classification System including the morphology, neurologic grade, and patient-specific modifiers. A grade of zero was considered to be not severe at all, and a grade of 100 was the most severe injury possible. Results A total of 74 AOSpine surgeons from all six AO regions of the world numerically graded the severity of each variable of the AOSpine Thoracolumbar Spine Injury Classification System to establish the injury severity score. The reported fracture severity increased significantly ( p < 0.0001) as the subtypes of fracture type A and type B increased, and a significant difference ( p < 0.0001) in severity was established for burst fractures with involvement of two versus one end plates. Finally, no regional or experiential difference in severity or classification was identified. Conclusion Development of a globally applicable injury severity scoring system for thoracolumbar trauma is possible. This study demonstrates no regional or experiential difference in perceived severity or thoracolumbar spine trauma. The AOSpine Thoracolumbar Spine Injury Classification System provides a logical approach to assessing these injuries and enables rational strategies for treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".