Correlation of Ordered Cervical Spine X-rays in Emergency Department with NEXUS and Canadian C-Spine Rules; a Clinical Audit
Bibliographic record
Abstract
Introduction: Evaluation of cervical spine injuries makes up a major part of trauma patient assessments. Based on the existing sources, more than 98% of the cervical spine X-rays show no positive findings. Therefore, the present clinical audit aimed to evaluate the correlation of ordered cervical spine X-rays in multiple trauma patients with NEXUS and Canadian c-spine clinical decision rules. Methods: The present clinical audit, evaluated the correlation of cervical spine imaging orders in multiple trauma patients presented to the emergency department, with NEXUS and Canadian c-spine rules. Initially, in a pilot study, the mentioned correlation was evaluated, and afterwards the results of this phase was analyzed. Since the correlation was low, an educational training was planned for all the physicians in charge. Finally, the calculated correlations for before and after training were compared using SPSS version 21. Results: Before and after training, cervical spine X-ray was ordered for 98 (62.82%) and 85 (54.48%) patients, respectively. Accuracy of cervical spine X-ray orders, based on the standard clinical decision rules, increased from 100 (64.1%) cases before training, to 143 (91.7%) cases after training (p < 0.001). Area under the receiver operating characteristic (ROC) curve regarding the correlation also raised from 52 (95% confidence interval (CI): 43 – 61) to 92 (95% CI: 87 – 97). Conclusion: Teaching NEXUS and Canadian c-spine clinical decision rules plays a significant role in improving the correlation of cervical spine X-ray orders in multiple trauma patients with the existing standards.
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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 | 0.008 | 0.035 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".