Comparison of nexus low risk criteria and Canadian cervical spine rule in blunt neck trauma
Bibliographic record
Abstract
BackgroundThe Canadian C-Spine (cervical-spine) Rule (CCR) and the National Emergency X-Radiography Utilization Study (NEXUS) Low-Risk Criteria (NLC) are guidelines for the use of cervical-spine radiography in patients with blunt neck trauma and polytrauma. It is unclear how the two decision rules compare in terms of clinical performance in our setting. MethodWe conducted a prospective observational study in 150 patients in emergency department of IOM TUTH comparing the CCR and NLC as applied to patients with blunt neck trauma and polytrauma. The sensitivity, specificity, and reduction in radiographs were analysed and compared. ResultAmong the 150 patients, the CCR was more sensitive than the NLC (100 % vs. 83.33%) and more specific (47.9 % vs. 42.3%) for injury, and its use would have resulted in lower radiography rates by (46 % vs. 42.33%). Using CCR no potential clinically important cervical spine injuries was missed but using NLC one clinically important cervical spine injury was missed. ConclusionFor blunt neck trauma and polytrauma patients who are in stable condition, the CCR is superior to the NLC with respect to sensitivity and specificity for cervical-spine injury, and its use would result in reduced rates of radiography. Further studies in larger sample size 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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".