Application of the Canadian C-Spine rule and nexus low criteria and results of cervical spine radiography in emergency condition
Bibliographic record
Abstract
INTRODUCTION: The Canadian C Spine Rule (CCR) and the National Emergency X-Radiography Utilization Study (Nexus) low criteria are well accepted as guide to help physician in case of cervical blunt trauma. METHODS: We aimed to evaluate retrospectively the application of these recommendations in our emergency department. Secondly we analyzed the quality of cervical spine radiography (CSR) in an emergency setting. RESULTS: 281 patients with cervical blunt trauma were analyzed retrospectively. The CCR and the NEXUS rules were respected in 91.2% and 96.8% of cases respectively. No lesions were found in 96.4% of patient. A lesion was present in 1.1% of patient and suspected in 2.5% of patient. The quality of CSR was adequate in only 37.7% of patient. The poor quality of CSR was due either to the lack of C7 vertebrae visualization in 64.6% or other lower vertebrae in 28%. Other causes included the absence of open mouth view (8%), the absence C1 vertebrae visualization (3.4%), artifact in 2.3% and the absence of lateral view in 0.6% of patient. CONCLUSION: CCR and NEXUS are widely used in our emergency department. The high rate of inadequate CSR reinforces the debate about it's utility in emergency condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".