AOSpine Thoracolumbar Spine Injury Classification System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it