Description and Reliability of the AOSpine Sacral Classification System
Bibliographic record
Abstract
BACKGROUND: Several classification systems exist for sacral fractures; however, these systems are primarily descriptive, are not uniformly used, have not been validated, and have not been associated with a treatment algorithm or prognosis. The goal of the present study was to demonstrate the reliability of the AOSpine Sacral Classification System among a group of international spine and trauma surgeons. METHODS: A total of 38 sacral fractures were reviewed independently by 18 surgeons selected from an expert panel of AOSpine and AOTrauma members. Each case was graded by each surgeon on 2 separate occasions, 4 weeks apart. Intrarater reproducibility and interrater agreement were analyzed with use of the kappa statistic (κ) for fracture severity (i.e., A, B, and C) and fracture subtype (e.g., A1, A2, and A3). RESULTS: Seventeen reviewers were included in the final analysis, and a total of 1,292 assessments were performed (646 assessments performed twice). Overall intrarater reproducibility was excellent (κ = 0.83) for fracture severity and substantial (κ = 0.71) for all fracture subtypes. When comparing fracture severity, overall interrater agreement was substantial (κ = 0.75), with the highest agreement for type-A fractures (κ = 0.95) and the lowest for type-C fractures (κ = 0.70). Overall interrater agreement was moderate (κ = 0.58) when comparing fracture subtype, with the highest agreement seen for A2 subtypes (κ = 0.81) and the lowest for A1 subtypes (κ = 0.20). CONCLUSIONS: To our knowledge, the present study is the first to describe the reliability of the AOSpine Sacral Classification System among a worldwide group of expert spine and trauma surgeons, with substantial to excellent intrarater reproducibility and moderate to substantial interrater agreement for the majority of fracture subtypes. These results suggest that this classification system can be reliably applied to sacral injuries, providing an important step toward standardization of treatment.
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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 | 0.046 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".