Utility of computed tomographic imaging of the cervical spine in trauma evaluation of ground-level fall
Bibliographic record
Abstract
BACKGROUND: Computed tomography (CT) of the cervical spine (C-spine) is routinely ordered for low-risk mechanisms of injury, including ground-level fall. Two commonly used clinical decision rules (CDRs) to guide C-spine imaging in trauma are the National Emergency X-Radiography Utilization Study (NEXUS) and the Canadian Cervical Spine Rule for Radiography (CCR). METHODS: Retrospective cross-sectional study of 3,753 consecutive adult patients presenting to an urban Level I emergency department who received C-spine CT scans were obtained over a 6-month period. The primary outcome of interest was prevalence of C-spine fracture. Secondary outcomes included fracture stability, appropriateness of imaging by NEXUS and CCR criteria, and estimated radiation dose exposure and costs associated with C-spine imaging studies. RESULTS: Of the 760 patients meeting inclusion criteria, 7 C-spine fractures were identified (0.92% ± 0.68%). All fractures were identified by NEXUS and CCR criteria with 100% sensitivity. Of all these imaging studies performed, only 69% met NEXUS indications for imaging (50% met CCR indications). C-spine CT scans in patients not meeting CDR indications were associated with costs of $15,500 to $22,000 by NEXUS ($14,600-$25,600 by CCR) in this single center during the 6-month study period. CONCLUSION: For ground-level fall, C-spine CT is overused. The consistent application of CDR criteria would reduce annual nationwide imaging costs in the United States by $6.8 to $9.6 million based on NEXUS ($6.4-$15.6 million based on CCR) and would reduce population radiation dose exposure by 0.8 to 1.1 million mGy based on NEXUS (0.7-1.9 million mGy based on CCR) if applied across all Level I trauma centers. Greater use of evidence-based CDRs plays an important role in facilitating emergency department patient management and reducing systemwide radiation dose exposure and imaging expenditures. LEVEL OF EVIDENCE: Diagnostic study, level III.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".