The AO spine upper cervical injury classification system: Do work setting or trauma center affiliation affect classification accuracy or reliability?
Bibliographic record
Abstract
PurposeTo assess the accuracy and reliability of the AO Spine Upper Cervical Injury Classification System based on a surgeons’ work setting and trauma center affiliation.MethodsA total of 275 AO Spine members participated in a validation of 25 upper cervical spine injuries, which were evaluated by computed tomography (CT) scans. Each participant was grouped based on their work setting (academic, hospital-employed, or private practice) and their trauma center affiliation (Level I, Level II or III, and Level IV or no trauma center). The classification accuracy was calculated as percent of correct classifications, while interobserver reliability, and intraobserver reproducibility were evaluated based on Fleiss’ Kappa coefficient.ResultsThe overall classification accuracy for surgeons affiliated with a level I trauma center was significantly greater than participants affiliated with a level II/III center or a level IV/no trauma center on assessment one (p1<0.0001) and two (p2 = 0.0003). On both assessments, surgeons affiliated with a level I or a level II/III trauma center were significantly more accurate at identifying IIIB injury types (p1 = 0.0007; p2 = 0.0064). Academic surgeons and hospital employed surgeons were significantly more likely to correctly classify type IIIB injuries on assessment one (p1 = 0.0146) and two (p2 = 0.0015). When evaluating classification reliability, the largest differences between work settings and trauma center affiliations was identified in type IIIB injuries.ConclusionType B injuries are the most difficult injury type to correctly classify. They are classified with greater reliability and classification accuracy when evaluated by academic surgeons, hospital-employed surgeons, and surgeons associated with higher-level trauma centers (I or II/III).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".