What Factors Influence Surgeons in Decision-Making in Thoracolumbar Burst Fractures? A Survey-Based Investigation of a Panel of Spine Surgery Experts
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional survey study. OBJECTIVE: To investigate factors affecting decision-making in thoracolumbar burst-fractures without neurologic deficit. METHODS: A 40-question survey addressing expert-related, economic, and radiological factors was distributed to 30 international trauma experts. Descriptive statistics were used to assess the impact of these factors on operative or non-operative management preferences. RESULTS: Out of 30 experts, 27 completed the survey. The majority of respondents worked at level 1 trauma centers (81.5%) within university settings (77.8%). They were primarily orthopedic surgeons (66.7%) and had over 10 years of experience (70.4%). About 81% found distinguishing between A3 and A4 fractures relevant for decision-making. Most experts (59%) treated A3 fractures non-surgically, while only 30% treated A4 fractures conservatively. Compensation systems did not influence treatment recommendations, and hospital measures promoting surgeries did not significantly affect distribution. Radiological factors, such as local kyphosis (25/27), fracture comminution (23/27), overall sagittal balance (21/27), and spinal canal narrowing (20/27), influenced decisions. CONCLUSION: Incomplete burst fractures (A3) are predominantly treated non-surgically, while complete burst fractures (A4) are primarily treated surgically. Compensation, third-party incentives, and outpatient care did not significantly impact decision-making. Radiological factors beyond the AO Spine thoracolumbar classification system seem to be essential and warrant further evaluation.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".