Application of AOSpine Subaxial Cervical Spine Injury Classification in Simple and Complex Cases
Bibliographic record
Abstract
INTRODUCTION: Cervical spine injury classification systems should be simple, easy to relate and remember, reliable guide for surgical planning, and predictor of outcome in clinical settings. We investigated whether the AOSpine subaxial cervical spine classification system predicted injury severity and neurologic outcome. MATERIAL AND METHODS: We analyzed the relevant clinical, imaging, management, and American Spinal Injury Association (ASIA) impairment scale (AIS) grade conversion of 92 AIS grades A-C patients with cervical spine injury. We correlated morphology class with age, injury severity score (ISS), follow-up ASIA motor score (AMS), intramedullary lesion length (IMLL), and AIS grade conversion at 6 months after injury. RESULTS: The mean age of patients was 39.3 years, 83 were men, and 69 were injured during an automobile accident or after a fall. The AOSpine class was A4 in 8, B2 in 5, B2A4 in 16, B3 in 19, and C in 44 patients. The mean ISS was 29.7 and AMS was 17.1. AIS grade was A in 48, B in 25, and C in 19 patients. Mean IMLL on postoperative magnetic resonance imaging was 72 mm: A4 = 68.1; B2A4 = 86.5; B2 = 59.3; B3 = 46.8; and C = 79.9. At a mean follow-up of 6 months, the mean AMS was 39.6. Compared to patients with class B3 injuries, those with class C injuries were significantly younger (P < 0.0001), had longer IMLL (P < 0.002), and were less likely to have AIS grade conversion to a better grade (P < 0.02). CONCLUSIONS: The AOSpine subaxial cervical spine injury classification system successfully predicted injury severity (longer IMLL) and chances of neurologic recovery (AIS grade conversion) across different class subtypes.
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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".