Is there a Regional Difference in Morphology Interpretation of A3/A4 Fractures among Different Cultures?
Bibliographic record
Abstract
Introduction The AOSpine Thoracolumbar Spine Injury Classification System was recently published, but before establishing a treatment algorithm to accompany the classification, further investigation on the cause of current regional treatment variations is required. The objective of this study is to determine if the ability of a surgeon to correctly classify A3 (burst fractures with a single endplate involved) and A4 (burst fractures with both end plates involved) fractures were affected by either the region or the experience of the surgeon. Material and Methods A survey was sent to 100 AOSpine members from all six AO regions of the world (North America, South America, Europe, Africa, Asia, and the Middle East) that had no prior knowledge of the new AOSpine Thoracolumbar Spine Injury Classification System. Respondents were asked to classify 25 cases, including 6 thoracolumbar burst fractures (A3 or A4). The current analysis focuses on the effect of region and experience on surgeons' ability to properly classify these controversial fractures. Results All 100 surveyed surgeons completed the survey, and no significant regional variability in the ability to correctly classify burst fractures was identified ( p > 0.50). Further analysis demonstrated that no region predisposed surgeons increasing the severity of burst fractures. Similarly, experience did not affect surgeons' ability to correctly classify burst fracture ( p > 0.21). Conclusion Regional variation in the treatment of thoracolumbar burst fractures (A3 and A4) is not because of the differing radiographic interpretation of the fractures.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".