Comparative Diagnostic Accuracy of the NEXUS Criteria and the Canadian C-Spine Rule in Cervical Spine Trauma
Bibliographic record
Abstract
Background & Objectives: Road traffic injuries are the fourth most common cause of death globally, according to surveys. The Canadian Cervical-Spine Rule (CCR) and the National Emergency X-Radiography Utilization Study (NEXUS) Low-Risk Criteria (NLC) are decision rules to guide the use of cervical-spine radiography in patients with trauma. In this study we aim to evaluate and compare the sensitivity and specificity of these rules in trauma patients for suspected C-spine injury. Materials & Methods: 500 patients were prospectively enrolled, in the event of them meeting the criteria. They were subjected to radiologic studies (X-ray or CT) of the cervical spine if they met NEXUS criteria or the CCR. Results: Of the 500 patients, 44.5% were subjected to radiography based on the NEXUS score and 58.8% based on the Canadian CCR. When the CCR was applied, the test was found to be 95.2% sensitive, 54.2% specific, 65% accurate, and with 42.6% positive predictive value and 97% negative predictive value. When NEXUS criteria were applied, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 100%, 75.3%, 59%, 100%, and 81.8%, respectively. Conclusion: When the NEXUS score was applied, the diagnostic accuracy was better. With the CCR, a greater number of patients were subjected to radiological evaluation. Either of the two criteria may be applied for emergency care in the Indian population to avoid unnecessary investigations. CCR followed by NEXUS criteria is recommended, and the utilization of the same is to be studied in a larger population.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".