경추 손상 환자에서 전산화 단층 촬영 시행을 위한 임상적 기준: NEXUS 기준과 Canadian cervical spine rule
Bibliographic record
Abstract
Purpose: National Emergency X-Radiography Utilization Study (NEXUS) criteria and the Canadian Cervical Spine rule (CCR) are commonly used in cervical trauma patients to determine whether a plain cervical X-ray should be performed. However, plain cervical X-rays are so inaccurate that cervical spine computed tomography (CT) is often considered as a screening test. We studied the usefulness of the NEXUS criteria and the CCR for determining the need for a CT evaluation in the emergency department (ED). Methods: This prospective observational study was conducted from January 2007 to March 2008. Plain X-ray and CT scans of the cervical spine were performed on blunt trauma patients with neck pain. The relevancy of CT was examined using the NEXUS criteria and the CCR. Sensitivity, specificity, positive predicted value, and negative predicted value analyses were performed to diagnose the cervical spine injury. Results: During the study period, 284 patients were enrolled in this study. The sensitivity, specificity, positive predicted value, and negative predicted value of the NEXUS criteria were 87.5%, 1.1%, 5.0%, and 60.0% respectively, while those of the CCR were 87.5%, 8.2%, 5.3%, and 91.6%. There were two missed fracture cases when the NEXUS criteria and the CCR were applied independently, however, no cases were missed when both were applied. Conclusion: This study suggests the NEXUS and the CCR in combination can be used as a guide to CT evaluation for cervical spine injury in the ED.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".