Accuracy of the Canadian C-spine rule and NEXUS to screen for clinically important cervical spine injury in patients following blunt trauma: a systematic review
Bibliographic record
Abstract
BACKGROUND: There is uncertainty about the optimal approach to screen for clinically important cervical spine (C-spine) injury following blunt trauma. We conducted a systematic review to investigate the diagnostic accuracy of the Canadian C-spine rule and the National Emergency X-Radiography Utilization Study (NEXUS) criteria, 2 rules that are available to assist emergency physicians to assess the need for cervical spine imaging. METHODS: We identified studies by an electronic search of CINAHL, Embase and MEDLINE. We included articles that reported on a cohort of patients who experienced blunt trauma and for whom clinically important cervical spine injury detectable by diagnostic imaging was the differential diagnosis; evaluated the diagnostic accuracy of the Canadian C-spine rule or NEXUS or both; and used an adequate reference standard. We assessed the methodologic quality using the Quality Assessment of Diagnostic Accuracy Studies criteria. We used the extracted data to calculate sensitivity, specificity, likelihood ratios and post-test probabilities. RESULTS: We included 15 studies of modest methodologic quality. For the Canadian C-spine rule, sensitivity ranged from 0.90 to 1.00 and specificity ranged from 0.01 to 0.77. For NEXUS, sensitivity ranged from 0.83 to 1.00 and specificity ranged from 0.02 to 0.46. One study directly compared the accuracy of these 2 rules using the same cohort and found that the Canadian C-spine rule had better accuracy. For both rules, a negative test was more informative for reducing the probability of a clinically important cervical spine injury. INTERPRETATION: Based on studies with modest methodologic quality and only one direct comparison, we found that the Canadian C-spine rule appears to have better diagnostic accuracy than the NEXUS criteria. Future studies need to follow rigorous methodologic procedures to ensure that the findings are as free of bias as possible.
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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 | 0.016 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".