Use of the canadian c-spine rule by emergency department nurses
Bibliographic record
Abstract
Background: Approximately 1.3 million Canadians present to the Emergency Department (ED) annually with a suspected cervical spine (c-spine) injury secondary to blunt force trauma. These patients are often alert and hemodynamically stable with less than 1% suffering a cervical spine fracture. Developed and validated at the Ottawa Research Institute, the Canadian C-Spine Rule is a highly sensitive clinical decision tool designed to mitigate the unnecessary use of radiography in alert, stable, trauma patients. Aim: The aim of this study is to explore the feasibility of developing a clinical practice guideline implementing the use of the Canadian C-Spine Rule by ED nurses working in Vancouver, British Columbia. Literature Review: Previous research demonstrates that when used by both ED physicians and registered nurses the Canadian C-Spine Rule enables accurate and reliable identification of trauma patients at low-risk for cervical spine injuries and is associated with decreased use of unwarranted radiography. In the ED, registered nurses do not routinely assess the cervical spine of alert, stable trauma patients; however, enabling nurses to remove cervical immobilization by utilizing the Canadian C-Spine Rule, could improve patient comfort, increase patient flow and optimize nurses’ roles and responsibilities in busy EDs. Methods: A short electronic survey was sent to members of the Vancouver General Hospital (VGH) ED Quality Council for input. Findings from this survey and informal interviews were utilized to perform a SWOT (Strength, Weakness, Opportunity, Threats) analysis related to educating local ED nurses to clear the cervical spine of low-risk trauma patients. Results: Respondents noted potential facilitators of this project included a strong nursing education team and highly motivated frontline nurses who are keenly interested in educational opportunities. Potential barriers identified within VGH ED could be a lack of physician support, nursing resistance due perceived increased workload, competing priorities for nursing education and a lack of low-risk trauma patients presenting to VGH ED immobilized in cervical collars. Conclusion: Prior to implementing this change into clinical practice, key recommendations based on stakeholder engagement and the SWOT analysis addressing internal and external factors facilitating or impeding practice change should be considered.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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".