Development of Online Technique for International Validation of the AO Spine Subaxial Injury Classification System
Bibliographic record
Abstract
STUDY DESIGN: Global cross-sectional survey. OBJECTIVE: To develop and refine the techniques for web-based international validation of fracture classification systems. METHODS: A live webinar was organized in 2018 for validation of the AO Spine Subaxial Injury Classification System, consisting of 35 unique computed tomography (CT) scans and key images with subaxial spine injuries. Interobserver reliability and intraobserver reproducibility was calculated for injury morphology, subtype, and facet injury according to the classification system. Based on the experiences from this webinar and incorporating rater feedback, adjustments were made in the organization and techniques used and in 2020 a repeat validation webinar was performed, evaluating images of 41 unique subaxial spine injuries. RESULTS: In the 2018 session, the AO Spine Subaxial Injury Classification System demonstrated fair interobserver reliability for fracture subtype (κ = 0.35) and moderate reliability for fracture morphology and facet injury (κ=0.45, 0.43, respectively). However, in 2020, the interobserver reliability for fracture morphology (κ = 0.87) and fracture subtype (κ = 0.80) was excellent, while facet injury was substantial (κ = 0.74). Intraobserver reproducibility for injury morphology (κ =0.49) and injury subtype/facet injury were moderate (κ = 0.42) in 2018. In 2020, fracture morphology and subtype reproducibility were excellent (κ =0.85, 0.88, respectively) while reproducibility for facet injuries was substantial (κ = 0.76). CONCLUSION: With optimized webinar-based validation techniques, the AO Spine Subaxial Injury Classification System demonstrated vast improvements in intraobserver reproducibility and interobserver reliability. Stringent fracture classification methodology is integral in obtaining accurate classification results.
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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 | 0.149 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".