The reliability of the AOSpine Thoracolumbar Spine Injury Classification System in children: An international validation study
Bibliographic record
Abstract
Purpose To evaluate the AOSpine Thoracolumbar Spine Injury Classification System and if it is reliable and reproducible when applied to the paediatric population globally. Methods A total of 12 paediatric orthopaedic surgeons were asked to review MRI and CT imaging of 25 paediatric patients with thoracolumbar spine traumatic injuries, in order to determine the classification of the lesions observed. The evaluators classified injuries into primary categories: A, B and C. Interobserver reliability was assessed for the initial reading by Fleiss's kappa coefficient (k F ) along with 95% confidence intervals (CI). For A and B type injuries, sub-classification was conducted including A0-A4 and B1-B2 subtypes. Interobserver reliability across subclasses was assessed using Krippendorff's alpha (α k ) along with bootstrapped 95% CIs. A second round of classification was performed one-month later. Intraobserver reproducibility was assessed for the primary classifications using Fleiss's kappa and sub-classification reproducibility was assessed by Krippendorff's alpha (α k ) along with 95% CIs. Results In total, 25 cases were read for a total of 300 initial and 300 repeated evaluations. Adjusted interobserver reliability was almost perfect (kF = 0.74; 95% CI 0.71 to 0.78) across all observers. Sub-classification reliability was substantial (α k = 0.67; 95% CI 0.51 to 0.81), Adjusted intraobserver reproducibility was almost perfect (kF = 0.91; 95% CI 0.83 to 0.99) for both primary classifications and for sub-classifications (α k = 0.88; 95% CI 0.83 to 0.93). Conclusion The inter- and intraobserver reliability for the AOSpine Thoracolumbar Spine Injury Classification System was high amongst paediatric orthopaedic surgeons. The AOSpine Thoracolumbar Spine Injury Classification System is a promising option as a uniform fracture classification in children. Level of Evidence III
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".