Does applying the Canadian Cervical Spine rule reduce cervical spine radiography rates in alert patients with blunt trauma to the neck? A retrospective analysis
Bibliographic record
Abstract
BACKGROUND: A cautious outlook towards neck injuries has been the norm to avoid missing cervical spine injuries. Consequently there has been an increased use of cervical spine radiography. The Canadian Cervical Spine rule was proposed to reduce unnecessary use of cervical spine radiography in alert and stable patients. Our aim was to see whether applying the Canadian Cervical Spine rule reduced the need for cervical spine radiography without missing significant cervical spine injuries. METHODS: This was a retrospective study conducted in 2 hospitals. 114 alert and stable patients who had cervical spine radiographs for suspected neck injuries were included in the study. Data on patient demographics, high risk & low risk factors as per the Canadian Cervical Spine rule and cervical spine radiography results were collected and analysed. RESULTS: 28 patients were included in the high risk category according to the Canadian Cervical Spine rule. 86 patients fell into the low risk category. If the Canadian Cervical Spine rule was applied, there would have been a significant reduction in cervical spine radiographs as 86/114 patients (75.4%) would not have needed cervical spine radiograph. 2/114 patients who had significant cervical spine injuries would have been identified when the Canadian Cervical Spine rule was applied. CONCLUSION: Applying the Canadian Cervical Spine rule for neck injuries in alert and stable patients would have reduced the use of cervical spine radiographs without missing out significant cervical spine injuries. This relates to reduction in radiation exposure to patients and health care costs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".