The Development of a Universally Accepted Sacral Fracture Classification: A Survey of AOSpine and AOTrauma Members
Bibliographic record
Abstract
Study Design Survey study. Objective To determine the global perspective on controversial aspects of sacral fracture classifications. Methods While developing the AOSpine Sacral Injury Classification System, a survey was sent to all members of AOSpine and AOTrauma. The survey asked four yes-or-no questions to help determine the best way to handle controversial aspects of sacral fractures in future classifications. Chi-square tests were initially used to compare surgeons’ answers to the four key questions of the survey, and then the data was modeled through multivariable logistic regression analysis. Results A total of 474 surgeons answered all questions in the survey. Overall 86.9% of respondents felt that the proposed hierarchical nature of injuries was appropriate, and 77.8% of respondents agreed that that the risk of neurologic injury is highest in a vertical fracture through the foramen. Almost 80% of respondents felt that the separation of injuries based on the integrity of L5–S1 facet was appropriate, and 83.8% of surgeons agreed that a nondisplaced sacral U fracture is a clinically relevant entity. Conclusion This study determines the global perspective on controversial areas in the injury patterns of sacral fractures and demonstrates that the development of a comprehensive and universally accepted sacral classification is possible.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".