The Influence of Regional Differences on the Reliability of the AO Spine Sacral Injury Classification System
Bibliographic record
Abstract
Study Design Global cross-sectional survey. Objective To explore the influence of geographic region on the AO Spine Sacral Classification System. Methods A total of 158 AO Spine and AO Trauma members from 6 AO world regions (Africa, Asia, Europe, Latin and South America, Middle East, and North America) participated in a live webinar to assess the reliability, reproducibility, and accuracy of classifying sacral fractures using the AO Spine Sacral Classification System. This evaluation was performed with 26 cases presented in randomized order on 2 occasions 3 weeks apart. Results A total of 8320 case assessments were performed. All regions demonstrated excellent intraobserver reproducibility for fracture morphology. Respondents from Europe (k = .80) and North America (k = .86) achieved excellent reproducibility for fracture subtype while respondents from all other regions displayed substantial reproducibility. All regions demonstrated at minimum substantial interobserver reliability for fracture morphology and subtype. Each region demonstrated >90% accuracy in classifying fracture morphology and >80% accuracy in fracture subtype compared to the gold standard. Type C morphology (p 2 = .0000) and A3 (p 1 = .0280), B2 (p 1 = .0015), C0 (p 1 = .0085), and C2 (p 1 =.0016, p 2 =.0000) subtypes showed significant regional disparity in classification accuracy (p 1 = Assessment 1, p 2 = Assessment 2). Respondents from Asia (except in A3) and the combined group of North, Latin, and South America had accuracy percentages below the combined mean, whereas respondents from Europe consistently scored above the mean. Conclusions In a global validation study of the AO Spine Sacral Classification System, substantial reliability of both fracture morphology and subtype classification was found across all geographic regions.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".