Bilateral facet effusion is a risk factor for segmental instability with cervical injury without vertebral fracture
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is effective in identifying cervical spine injury after trauma. However, cervical instability without major bone injury or dislocation is challenging to assess. Hence, the current study aimed to investigate and compare the MRI and radiography findings of segmental instability in patients with cervical spine injury. We investigated 34 participants with cervical spine injury without vertebral fracture. Based on the radiography findings, the participants were categorized into two: group A with segmental instability (n = 11) and group B without segmental instability (n = 23). Both groups were compared in terms of the presence of segmental instability on radiography and MRI. Anterior longitudinal ligament (ALL) injury, disc injury, and bilateral facet effusion were observed in 6/11, 5/11, and 7/11 patients in group A and in 5/23, 2/23 and 7/23 patients in group B, respectively. The results showed significant differences (p < 0.05). Moreover, 2 and 10 of 11 patients in group A and 16 and 7 of 23 patients in group B presented with hemi lateral facet effusion and paravertebral muscle injury, respectively. However, the results did not significantly differ. According to a logistic regression analysis, bilateral facet effusion after trauma was associated with cervical segmental instability (odd ratio: 10.6, 95% confidence interval: 1.31-84.7). Facet joint effusion might be caused by capsule injury during trauma. Most participants with segmental instability had ALL, disc, and flavum injury and bilateral facet effusion. Therefore, we need to consider bilateral facet effusion with other soft tissue damage of the cervical spine as an association factor to show the instability.
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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) | 0.000 | 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) | 0.002 | 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 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".