NEXUS vs. Canadian C-Spine Rule (CCR) in Predicting Cervical Spine Injuries; a Systematic Review and Meta-analysis.
Bibliographic record
Abstract
Introduction: Clinical decision tools have been shown to reduce imaging rates for clearance of suspected cervical spine injury (CSI). This review provides more comprehensive evidence on the diagnostic capabilities of National Emergency X-Radiography Utilization Study (NEXUS) and Canadian C-spine rule (CCR) in this regard. Method: , 2023, in databases of Medline, Scopus, Web of Science, and Embase, investigating the performance of NEXUS and CCR in blunt trauma patients. QUADAS-2 and GRADE guidelines were used to assess the quality and certainty of evidence. All analyses were performed using the STATA 14.0 statistical analysis software. Results: 35 articles comprising 70000 patients for NEXUS and 33000 patients for CCR were included in this review. NEXUS and CCR were evaluated to have a sensitivity of 0.94 (95% confidence interval (CI): 0.88 to 0.98) and 1.00 (95% CI: 0.98 to 1.00) in the detection of any CSI and 0.95 (95% CI: 0.89 to 0.98) and 1.00 (95% CI: 0.95 to 1.00) in the detection of clinically important CSI. The area under the curve (AUC) of NEXUS and CCR was 0.85 and 0.97 for any CSI and 0.78 (95% CI: 0.74 to 0.81) and 0.94 (95% CI: 0.91 to 0.96) for clinically important CSI. Conclusion: Our study demonstrates that both NEXUS and CCR can be used in ruling out patients with low risk of CSI, and CCR was shown to have superior performance. Even though these tools have low specificity, their application can still greatly reduce the number of radiographic imaging performed in emergency departments.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".