Reliability Analysis of the AOSpine thoracolumbar Spine Injury Classification System by a Worldwide Group of Naive Spinal Surgeons
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 worldwide applicability has yet to be established. Material and Methods A previously described classification system of thoracolumbar injuries which consists of a morphologic classification of the fracture, a grading system for the neurologic status and relevant patient-specific modifiers was applied to 25 cases by 100 spinal surgeons from across the world twice independently, in grading sessions 1 month apart. The results were analyzed for classification reliability using the Kappa coefficient (κ). Results The overall Kappa coefficient for all cases was 0.56 which represents moderate reliability. Kappa values describing interobserver agreement were 0.80 for type A injuries, 0.68 for type B injuries, and 0.72 for type C injuries, all representing substantial reliability. The lowest level of agreement for specific subtypes was for fracture subtype A4 (Kappa = 0.19). Intraobserver analysis demonstrated overall average Kappa statistic for subtype grading of 0.68 also representing substantial reproducibility. Conclusion In a worldwide sample of spinal surgeons without previous exposure to the recently described AOSpine Thoracolumbar Spine Injury Classification System, we demonstrated moderate interobserver and substantial intraobserver reliability. These results suggest that most spine surgeons can reliably apply this system to spine trauma patients as or more reliably than previously described systems.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".