The Timing and Influence of MRI on the Management of Patients With Cervical Facet Dislocations Remains Highly Variable
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Traumatic cervical facet dislocations are potentially devastating injuries. Magnetic resonance imaging (MRI) is an excellent means of assessing ligamentous disruption, disk herniation, and compression of the neural elements. However, despite an improved understanding of these facet dislocations with imaging, treatment remains controversial. PURPOSE: To survey the timing and influence of MRI on the management of patients with traumatic cervical facet dislocations. STUDY DESIGN: Questionnaire study. METHODS: Clinical vignettes, plain radiographs, and computed tomography scans of 10 cases of cervical facet dislocation were presented to 25 fellowship trained spine surgeons. Participants were analyzed as to their next step in diagnosis or treatment: closed reduction, obtaining an MRI, or proceeding directly with open treatment. A revised vignette was then presented; however, on this occasion, an MRI was included with the imaging and had been obtained before a reduction attempt. Participants were then surveyed on their choice of closed or open reduction. Each of the vignettes consisted of 3 different clinical scenarios based on neurologic examination: intact, incomplete, or complete spinal cord injury. RESULTS: The interrater reliability of treatment decisions was very poor, and the reliability after MRI was available and was significantly worse when the patient was considered to have a complete spinal cord injury. After reviewing the MRI, orthopedic surgeons were significantly more likely to choose a closed versus open reduction. Neurosurgeons were significantly more likely than orthopedic surgeons to order an MRI before open or closed treatment. CONCLUSIONS: The timing and utilization of MRI for patients with traumatic cervical facet dislocations remains variable. Further outcome analysis in the form of evidence-based algorithms is necessary to optimize patient management and outcomes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it