Canadian Cervical Spine Rule Compared With Computed Tomography: A Prospective Analysis
Bibliographic record
Abstract
BACKGROUND: The Canadian cervical spine rule (CCS) has been found to be an effective tool to determine the need for radiographic evaluation of the cervical spine (c-spine) incorporating both clinical findings and mechanism. Previously, it has been validated only through clinical follow-up or selective use of X-rays. The purpose of this study was to validate it using computed tomography (CT) as the gold standard to identify fractures. METHODS: Prospective evaluation was performed on 3,201 blunt trauma patients who were screened by CCS and were compared with a complete c-spine CT. CSS positive indicated at least one positive clinical or mechanism finding, whereas CT positive indicated presence of a fracture. RESULTS: There were 192 patients with c-spine fractures versus 3,009 without fracture on CT. The fracture group was older (42.7 ± 19.0 years vs. 37.8 ± 17.5 years, p = 0.0006), had a lower Glasgow Coma Scale score (13.8 ± 4.2 vs. 14.4 ± 4.3, p < 0.0001), and lower systolic blood pressure (133.3 ± 23.8 mm Hg vs. 139.5 ± 23.1 mm Hg, p = 0.0023). The sensitivity of CCS was 100% (192/192), specificity was 0.60% (18/3009), positive predictive value was 6.03% (192/3183), and negative predictive value was 100% (18/18). Logistic regression identified only 8 of the 19 factors included in the CCS to be independent predictors of c-spine fracture. CONCLUSIONS: CCS is very sensitive but not very specific to determine the need for radiographic evaluation after blunt trauma. Based on this study, the rule should be streamlined to improve specificity while maintaining sensitivity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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 | 0.000 |
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".