Emergency radiology: straightening of the cervical spine in MDCT after trauma—a sign of injury or normal variant?
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether straightening of the cervical spine (C-spine) alignment after trauma can be considered a significant multidetector CT (MDCT) finding. METHODS: 160 consecutive patients after C-spine trauma admitted to a Level 1 trauma centre received MDCT according to Canadian Cervical Spine Rule and National Emergency X-Radiography Utilization Study indication rule; subgroups with and without cervical collar immobilization (CCI +/-) were compared with a control group (n = 20) of non-traumatized patients. Two independent readers evaluated retrospectively the alignment, determined the absolute rotational angle of the posterior surface of C2 and C7 (ARA C2-7) and grouped the results for lordosis (<-13°), straight (-13 to +6°) and kyphosis (>+6°). RESULTS: In the two CCI-/CCI+ study groups, the straight or kyphotic alignment significantly (p = 0.001) predominated over lordosis. The number of patients with straight C-spine alignment was higher in the CCI+ group (CCI+ 69% vs CCI- 49%, p = 0.05). A comparison of the CCI+ group vs the CCI- group revealed a slightly smaller number of kyphotic (10% vs 18%, p = 0.34) and lordotic (21% vs 33%, p = 0.33) alignments. Statistically, however, the differences were of no significance. The control group revealed no significant differences. CONCLUSION: Straightening of the C-spine alone is not a definitive sign of injury but is a biomechanical variation due to CCI and neck positioning during MDCT or active patient control. ADVANCES IN KNOWLEDGE: Straightening of the C-spine alignment in MDCT alone is not a definitive sign of injury. Straightening of the C-spine alignment is related to neck positioning and active patient control. CCI has a straightening effect on the cervical alignment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".