Variations in Surgical Treatment of Cervical Facet Dislocations
Bibliographic record
Abstract
STUDY DESIGN: Retrospective Survey Analysis. OBJECTIVE: To explore surgeon preference in the choice of surgical approach in the treatment of traumatic cervical facet dislocations. SUMMARY OF BACKGROUND DATA: The choice of surgical approach in the treatment of traumatic cervical dislocations is highly variable and maybe influenced by a variety of factors. The purpose of this study was to examine inter-rater reliability in choice of surgical approach. METHODS: Twenty-five members of the Spine Trauma Study Group evaluated 10 cases of traumatic cervical dislocations. Evaluation of the case as a unilateral or bilateral injury and surgeon interpretation of the presence of a disc herniation as well as preferred surgical approach were assessed. RESULTS: Only slight agreement was observed among surgeons in the choice of surgical approach (Kappa < 0.1). This improved slightly when patients were assumed to have a complete spinal cord injury (Kappa = 0.15). Surgeons used more anterior approaches either alone or as the first stage in a combined approach when a disc herniation was present regardless of neurologic status of the patient. When a patient was neurologically intact, an anterior approach was more common than a posterior approach even when a disc herniation was not present. Combined approaches were preferred for the treatment of bilateral facet dislocations. CONCLUSION: The poor agreement on the treatment of these injuries likely reflects a combination of factors including surgeon training and experience. Treatment decisions are likely to be affected by the neurologic status of the patient, interpretation of a disc herniation, and the classification of the injury as a unilateral or bilateral injury.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".