AOSpine Thoracolumbar Spine Injury Classification System
Bibliographic record
Abstract
STUDY DESIGN: Reliability and agreement study, retrospective case series. OBJECTIVE: To develop a widely accepted, comprehensive yet simple classification system with clinically acceptable intra- and interobserver reliability for use in both clinical practice and research. SUMMARY OF BACKGROUND DATA: Although the Magerl classification and thoracolumbar injury classification system (TLICS) are both well-known schemes to describe thoracolumbar (TL) fractures, no TL injury classification system has achieved universal international adoption. This lack of consensus limits communication between clinicians and researchers complicating the study of these injuries and the development of treatment algorithms. METHODS: A simple and reproducible classification system of TL injuries was developed using a structured international consensus process. This classification system consists of a morphologic classification of the fracture, a grading system for the neurological status, and description of relevant patient-specific modifiers. Forty cases with a broad range of injuries were classified independently twice by group members 1 month apart and analyzed for classification reliability using the Kappa coefficient (κ). RESULTS: The morphologic classification is based on 3 main injury patterns: type A (compression), type B (tension band disruption), and type C (displacement/translation) injuries. Reliability in the identification of a morphologic injury type was substantial (κ= 0.72). CONCLUSION: The AOSpine TL injury classification system is clinically relevant according to the consensus agreement of our international team of spine trauma experts. Final evaluation data showed reasonable reliability and accuracy, but further clinical validation of the proposed system requires prospective observational data collection documenting use of the classification system, therapeutic decision making, and clinical follow-up evaluation by a large number of surgeons from different countries.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".