Clinical and Radiological Clues of Traumatic Craniocervical Junction Injuries Requiring Occipitocervical Fusion to Early Diagnosis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to find the clinical and radiographic characteristics of traumatic craniocervical junction (CCJ) injuries requiring occipitocervical fusion (OC fusion) for early diagnosis and surgical intervention. METHODS: We retrospectively reviewed 12 patients with CCJ injuries presenting to St. Michaels Hospital in Toronto who underwent OC fusion and looked into the following variables; (1) initial trauma data on emergency room arrival, (2) associated injuries, (3) imaging characteristics of computed tomography (CT) scan and magnetic resonance imaging (MRI), (4) surgical procedures, surgical complications, and neurological outcome. RESULTS: All patients were treated as acute spinal injuries and underwent OC fusion on an emergency basis. Patients consisted of 10 males and 2 females with an average age of 47 years (range, 18-82 years). All patients sustained high-energy injuries. Three patients out of 6 patients with normal BAI (basion-axial interval) and BDI (basion-dens interval) values showed visible CCJ injuries on CT scans. However, the remaining 3 patients had no clear evidence of occipitoatlantal instability on CT scans. MRI clearly described several findings indicating occipitoatlantal instability. The 8 patients with normal values of ADI (atlantodens interval interval) demonstrated atlantoaxial instability on CT scan, however, all MRI more clearly and reliably demonstrated C1/2 facet injury and/or cruciate ligament injury. CONCLUSION: We advocate measures to help recognize CCJ injury at an early stage in the present study. Occipitoatlantal instability needs to be carefully investigated on MRI in addition to CT scan with special attention to facet joint and ligament integrity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".