An international validation of the AO spine subaxial injury classification system
Bibliographic record
Abstract
PURPOSE: To validate the AO Spine Subaxial Injury Classification System with participants of various experience levels, subspecialties, and geographic regions. METHODS: A live webinar was organized in 2020 for validation of the AO Spine Subaxial Injury Classification System. The validation consisted of 41 unique subaxial cervical spine injuries with associated computed tomography scans and key images. Intraobserver reproducibility and interobserver reliability of the AO Spine Subaxial Injury Classification System were calculated for injury morphology, injury subtype, and facet injury. The reliability and reproducibility of the classification system were categorized as slight (ƙ = 0-0.20), fair (ƙ = 0.21-0.40), moderate (ƙ = 0.41-0.60), substantial (ƙ = 0.61-0.80), or excellent (ƙ = > 0.80) as determined by the Landis and Koch classification. RESULTS: A total of 203 AO Spine members participated in the AO Spine Subaxial Injury Classification System validation. The percent of participants accurately classifying each injury was over 90% for fracture morphology and fracture subtype on both assessments. The interobserver reliability for fracture morphology was excellent (ƙ = 0.87), while fracture subtype (ƙ = 0.80) and facet injury were substantial (ƙ = 0.74). The intraobserver reproducibility for fracture morphology and subtype were excellent (ƙ = 0.85, 0.88, respectively), while reproducibility for facet injuries was substantial (ƙ = 0.76). CONCLUSION: The AO Spine Subaxial Injury Classification System demonstrated excellent interobserver reliability and intraobserver reproducibility for fracture morphology, substantial reliability and reproducibility for facet injuries, and excellent reproducibility with substantial reliability for injury subtype.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".