Radiological Improvements and Clinical Outcomes of Occipitocervical Fusion for Traumatic Cranocervical Junction Instability
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the radiological outcomes of occipitocervical fusion (OCF) for traumatic craniocervical junction (CCJ) instability by analyzing changes in preoperative and postoperative computed tomography (CT)-measured radiological parameters. METHODS: We retrospectively analyzed 20 consecutive adult trauma patients who underwent OCF for CCJ instability at a single center from January 2015 to May 2023. Clinical features, surgical outcomes, and preoperative and postoperative CT-measured parameters (basion-dens interval [BDI], basion-axial interval [BAI], atlanto-dental interval [ADI], space available for the cord, clivus-canal angle) were evaluated. Patients were grouped according to whether their preoperative parameter values were within or outside the normal range, and changes were compared between the groups. RESULTS: All patients underwent OCF without neurological deterioration, except for 1 death from polytrauma. Significant postoperative improvements were observed in BDI for patients with abnormal preoperative BDI >8.5 mm (-4.27 ± 3.67 mm) compared to those with normal BDI <8.5 mm (0.11 ± 1.84 mm, P = 0.0194), and in ADI for those with abnormal preoperative ADI >2 mm (-1.88 ± 0.61 mm vs. 0.02 ± 0.16 mm, P = 0.0011). BAI improved significantly in patients with abnormal preoperative BAI < -4 mm (9.07 ± 5.74 mm, P = 0.0154) and >12 mm (-8.45 ± 4.65 mm, P = 0.0078) compared to those within normal limits. Space available for the cord (<14 mm) and clivus-canal angle (>160° or <145°) showed trends toward improvement but without statistical significance. Postoperative complications included dysphagia (10%), hardware failure (10%), and surgical site infection (5%). CONCLUSIONS: OCF effectively stabilizes traumatic CCJ instability, improves key CT-measured radiological parameters, and supports favorable neurological outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".