Establishing the Injury Severity of Subaxial Cervical Spine Trauma
Bibliographic record
Abstract
STUDY DESIGN: Global cross-sectional survey. OBJECTIVE: The aim of this study was to validate the AO Spine Subaxial Cervical Spine Injury Classification by examining the perceived injury severity by surgeon across AO geographical regions and practice experience. SUMMARY OF BACKGROUND DATA: Previous subaxial cervical spine injury classifications have been limited by subpar interobserver reliability and clinical applicability. In an attempt to create a universally validated scheme with prognostic value, AO Spine established a subaxial cervical spine injury classification involving four elements: injury morphology, facet injury involvement, neurologic status, and case-specific modifiers. METHODS: A survey was sent to 272 AO Spine members across all geographic regions and with a variety of practice experience. Respondents graded the severity of each variable of the classification system on a scale from zero (low severity) to 100 (high severity). Primary outcome was to assess differences in perceived injury severity for each injury type over geographic regions and level of practice experience. RESULTS: A total of 189 responses were received. Overall, the classification system exhibited a hierarchical progression in subtype injury severity scores. Only three subtypes showed a significant difference in injury severity score among geographic regions: F3 (floating lateral mass fracture, P = 0.04), N3 (incomplete spinal cord injury, P = 0.03), and M2 (critical disk herniation, P = 0.04). When stratified by surgeon experience, pairwise comparison showed only two morphological subtypes, B1 (bony posterior tension band injury, P = 0.02) and F2 (unstable facet fracture, P = 0.03), and one neurologic subtype (N3, P = 0.02) exhibited a significant difference in injury severity score. CONCLUSION: The AO Spine Subaxial Cervical Spine Injury Classification System has shown to be reliable and suitable for proper patient management. The study shows this classification is substantially generalizable by geographic region and surgeon experience, and provides a consistent method of communication among physicians while covering the majority of subaxial cervical spine traumatic injuries.Level of Evidence: 4.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".