Expert Opinion, Real-World Classification, and Decision-Making in Thoracolumbar Burst Fractures Without Neurologic Deficits?
Bibliographic record
Abstract
STUDY DESIGN: Retrospective analysis of prospectively collected data. OBJECTIVES: To compare decision-making between an expert panel and real-world spine surgeons in thoracolumbar burst fractures (TLBFs) without neurological deficits and analyze which factors influence surgical decision-making. METHODS: This study is a sub-analysis of a prospective observational study in TL fractures. Twenty two experts were asked to review 183 CT scans and recommend treatment for each fracture. The expert recommendation was based on radiographic review. RESULTS: Overall agreement between the expert panel and real-world surgeons regarding surgery was 63.2%. In 36.8% of cases, the expert panel recommended surgery that was not performed in real-world scenarios. Conversely, in cases where the expert panel recommended non-surgical treatment, only 38.6% received non-surgical treatment, while 61.4% underwent surgery. A separate analysis of A3 and A4 fractures revealed that expert panel recommended surgery for 30% of A3 injuries and 68% of A4 injuries. However, 61% of patients with both A3 and A4 fractures received surgery in the real world. Multivariate analysis demonstrated that a 1% increase in certainty of PLC injury led to a 4% increase in surgery recommendation among the expert panel, while a .2% increase in the likelihood of receiving surgery in the real world. CONCLUSION: Surgical decision-making varied between the expert panel and real-world treating surgeons. Differences appear to be less evident in A3/A4 burst fractures making this specific group of fractures a real challenge independent of the level of expertise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".