Comparing the Canadian Cervical Spine Rule and Radiology Confirmed Cervical Spine Injury in the Emergency Department
Bibliographic record
Abstract
In the United States and Canada, more than 13 million trauma patients are at risk for cervical spine injury (CSI). To date, providers use computerized tomography (CT) to rule out CSI in these patients. Cervical spine immobilization with the use of rigid cervical collars can be uncomfortable, delays hospital transport, and has a modest morbidity. The Canadian Cervical Spine Rule (CCR) is a screening tool used in emergency departments (ED) as a screening tool to rule out CSI to avoid unnecessary CTs. The purpose of this project is to complete a retrospective chart review to evaluate if emergency departments can apply the CCR to reduce cervical spine radiographs in the adult patient with suspected CSI. The study was a retrospective chart review, which took place at a level 2 trauma center emergency department in a community teaching hospital reviewing patient records and radiological findings in relation to the potential benefit of using the CCR in CSI assessment. The sample was a non-probability convenience sample of patients who have had a cervical spine radiograph in the ED. One hundred charts were reviewed and 60 patient records were included in the study. Of the 60 records included in the study, half met high risk criteria requiring cervical spine radiography. Of the 30 records meeting low risk criteria, only 2 charts had complete documentation including neck range of motion (ROM). One had normal ROM and one was unable to actively rotate neck. There were 8 records that documented “normal range of motion” but did not specifically note normal neck ROM. It is difficult to definitively state that 28 CTs could have been prevented, as there was inadequate neck range of motion documentation. There were no cervical spine injuries detected by CT in this study. As with previous studies, the data shows that the CCR maintains its sensitivity but has low specificity. It remains that clinicians still rely on radiology evidence to rule out CSI rather than assessment tools.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".