Unilateral Cervical Facet Dislocation: Biomechanics of Fixation
Bibliographic record
Abstract
STUDY DESIGN: Unilateral facet dislocation was created in human cadaveric cervical spines. Specimens were sequentially instrumented with posterior or anterior screws and plates, and studied biomechanically. OBJECTIVE: To determine the biomechanical differences between anterior and posterior fixation for stabilization of a reduced unilateral cervical facet dislocation. SUMMARY OF BACKGROUND DATA: Although previous studies have compared anterior to posterior instrumentation, no data exist on the biomechanics of either type of stabilization after this particular injury. METHODS: In 6 human cadaveric cervical spine segments, a reproducible unilateral facet dislocation was created and then unlocked (reduced). Nondestructive torques were applied to specimens that were intact, injured-reduced, fixated using posterior nonlocking lateral mass plates, and fixated using a bone graft plus an anterior nonlocking plate. Flexion, extension, lateral bending, and axial rotation were measured stereophotogrammetrically. RESULTS: Lateral mass plating was more effective than anterior plating in limiting motion after reduction of a unilateral facet dislocation. Averaged, over all loading directions, lateral mass plates reduced the range of motion to 17% of normal; anterior plates reduced range of motion to 89% of normal. In all loading directions, lateral mass plates performed significantly better than anterior plates (P < 0.05, paired Student t-tests). CONCLUSIONS: Anterior and posterior plating effectively stabilized a reduced unilateral facet dislocation. Lateral mass fixation provided better immobilization than anterior graft and plate.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".