Global Validation of the AO Spine Upper Cervical Injury Classification: Geographic Region Affects Reliability and Reproducibility
Bibliographic record
Abstract
Study Design Global Survey Objective To determine the accuracy, interobserver reliability, and intraobserver reproducibility of the AO Spine Upper Cervical Injury Classification System based on surgeons’ AO Spine region of practice (Africa, Asia, Central/South America, Europe, Middle East, and North America). Methods A total of 275 AO Spine members assessed 25 upper cervical spine injuries and classified them according to the AO Spine Upper Cervical Injury Classification System. Reliability, reproducibility, and accuracy scores were obtained over two assessments administered at three-week intervals. Kappa coefficients (ƙ) determined the interobserver reliability and intraobserver reproducibility. Results On both assessments, participants from Europe and North America had the highest classification accuracy, while participants from Africa and Central/South America had the lowest accuracy ( P < .0001). Participants from Africa (assessment 1 (AS1):ƙ = .487; AS2:0.491), Central/South America (AS1:ƙ = .513; AS2:0.511), and the Middle East (AS1:0.591; AS2: .599) achieved moderate reliability, while participants from North America (AS1:ƙ = .673; AS2:0.648) and Europe (AS1:ƙ = .682; AS2:0.681) achieved substantial reliability. Asian participants obtained substantial reliability on AS1 (ƙ = .632), but moderate reliability on AS2 (ƙ = .566). Although there was a large effect size, the low number of participants in certain regions did not provide adequate certainty that AO regions affected the likelihood of participants having excellent reproducibility ( P = .342). Conclusions The AO Spine Upper Cervical Injury Classification System can be applied with high accuracy, interobserver reliability, and intraobserver reproducibility. However, lower classification accuracy and reliability were found in regions of Africa and Central/South America, especially for severe atlas injuries (IIB and IIC) and atypical hangman’s type fractures (IIIB injuries).
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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 | 0.002 | 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 | 0.000 | 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".