Utility of Helical Computed Tomography in Differentiating Unilateral and Bilateral Facet Dislocations
Bibliographic record
Abstract
OBJECTIVE: Diagnosis of cervical facet dislocation is difficult when relying on plain radiographs alone. This study evaluates the interobserver reliability of helical computed tomography (CT) in the assessment of cervical translational injuries, correlates the radiographic diagnosis with intraoperative observation, and examines the role of neurologic injury in the evaluation and diagnosis of these injuries. METHODS: Clinical histories and radiographic studies of 10 patients with cervical facet dislocations were presented to 25 surgeons. Participants classified cases as unilateral or bilateral facet dislocations after reviewing selected axial CT slices and sagittal reconstructions. Surgeons' interpretations were compared with intraoperative diagnosis. Participants interpreted the same radiographic studies with 3 different clinical scenarios: neurologically intact, incomplete, and complete spinal cord injury. Vertebral body translation from midsagittal CT was evaluated to confirm whether all unilateral facet dislocations had <25% translation. RESULTS: Interrater kappa coefficient showed moderate agreement between observers in classifying injuries as unilateral or bilateral (kappa: 0.54-0.58), regardless of neurologic status. Percent agreement among observers varied from 50% to 100% for each individual case. Agreement was statistically higher for bilateral facet dislocation (85%) than for unilateral dislocations (78%), with 1 unilateral fracture showing nearly 50% translation on a midsagittal image. CONCLUSIONS: The addition of helical CT to reconstruction enables spine surgeons to more reliably distinguish bilateral from unilateral cervical facet dislocations. Despite frequent occurrence of these injuries and presumed agreement on injury description, agreement may be improved by a more precise definition of facet dislocations and subluxations and thorough review of all imaging studies.
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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) | 0.001 | 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 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".