Cervical spine clearance after blunt trauma: current state of the art
Bibliographic record
Abstract
Abstract No definite consensus exists for the clearance of the cervical spine (C-spine) after blunt trauma, despite many validated algorithms, recommendations and guidelines. We intend to answer the most relevant questions with which physicians are confronted when clearing C-spines after blunt trauma in emergency departments (EDs). To exclude significant C-spine injuries we designed an algorithm to be compatible with clinical practice, to simplify patient management and avoid unrewarding evaluation. We conducted an exploratory PubMed search including articles published from January 2000 to October 2018. Keywords used were “cervical spine”, “injury”, “clearance”, “Canadian C-spine Rule”, “CCR” and “national emergency x-radiography utilization study”. Clinical and experimental studies were included in a detailed review. We based our literature review on 33 articles. While answering fundamental triage questions from daily clinical practice, the current literature is discussed in detail. We designed an algorithm for the C-spine clearance suitable for any trauma centre with a high-quality multiplanar reconstruction computerized tomography (CT) scan continuously available. The high sensitivity of the Canadian C-spine Rule (CCR) prevents missing C-spine injuries while limiting the amount of unnecessary radiologic examinations. Plain radiographs were fully abandoned for C-spine clearance. A negative CT scan is sufficient to clear the majority of C-spine injuries and allows for collar removal. In case of motor symptoms or radio-clinical discrepancy, the advice of a specialized spine surgeon must be requested. Magnetic resonance imaging must not be routinely used. Neck pain despite negative imaging is not a reason to delay removal of stiff cervical collars. Cite this article: EFORT Open Rev 2020;5:253-259. DOI: 10.1302/2058-5241.5.190047
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".