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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".