The Value of CT and MRI in the Evaluation and Management of Patients with Thoracolumbar Spinal Injuries
Bibliographic record
Abstract
Introduction Although imaging has a major role in evaluation and management of thoracolumbar spinal trauma, the exact role of CT and MRI in addition to radiographs for fracture classification and management is unclear. We conducted an online survey base study to evaluate the added value of computed tomography (CT) and magnetic resonance imaging (MRI) in classification, evaluation of stability and management of thoracolumbar injuries. Methods Spine surgeons ( n = 41) from around the world classified 30 thoracolumbar fractures. The cases were presented in a three step approach: first plain radiographs, followed by CT and MRI images. Surgeons were asked to classify according to the AO Spine Classification System, evaluate fracture stability and choose management. Results Surgeons correctly classified 43.4% of fractures with plain radiographs alone; after additionally evaluating CT and MRI images, this percentage increased by further 18.2% and 2.2% respectively. Instability was diagnosed in 68.5% cases with plain radiographs and this percentage increased to 79.3% after CT ( p < .0001) but did not increase significantly after MRI. AO Type A fractures were identified in 51.7% of fractures with radiographs while the number of type B fractures increased after CT and MRI. The number of type C fractures diagnosed was constant across the three steps. Agreement between radiographs and CT was fair for A-type (k=0.31), poor for B-type (k=0.19), but it was excellent between CT and MRI (k > 0.87). CT and MRI had similar sensitivity in identifying fracture sub types except that MRI had a higher sensitivity (56.5%) for B2 fractures ( p < 0.001). Conclusion For accurate classification, radiographs alone were insufficient except for C type injuries. CT is mandatory for accurately classifying thoracolumbar fractures. Though MRI did confer a modest gain in sensitivity in B2 injuries, the study does not support the need for routine MRI in patients without spinal cord injury for classification, assessing instability or need for surgery.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".