Retrospective Analysis of Efficacy of the National Emergency X-Radiography Utilization Study Low-Risk Criteria and the Canadian Cervical Spine Rules for Cervical Spine Trauma
Bibliographic record
Abstract
Background: With increasing road traffic accidents, cervical spine injuries are a major health hazard in the developed as well as the developing world. Over the years, the National Emergency X-radiography Utilization Study (NEXUS) low-risk criteria and the Canadian cervical spine rules (CCRs) have acted as primary guidelines in emergency departments around the world to decide on the need for cervical spine X-ray in emergency settings. The aim of this study was to retrospectively analyze the efficiency of both the NEXUS low-risk criteria and CCR in confirming positive cervical spine injuries in emergency department settings. Aims and Objectives: The aim was to retrospectively analyze the efficiency of both the NEXUS low-risk criteria and CCR in confirming positive cervical spine injuries. Methods: A retrospective study involving 631 patients for 4 years aged above 18 years, who underwent a cervical spine X-ray from June 2018 to June 2022, were included in the study. From the eligible case records, the data pertaining to the NEXUS low-risk criteria and CCR were recorded. Along with this, the final diagnosis regarding the cervical spine injury, confirmed by subsequent computed tomography (CT) scan or magnetic resonance imaging (MRI), was also recorded. Results: The NEXUS low-risk criteria and CCR were met in 92.7% and 98.6% of the patients, respectively. The cervical spine X-rays were normal in 87.8% of the patients, fractures were recorded in 9.5% of the patients, and in 2.7% of the patients, doubtful lesions were present, which needed additional investigations in the form of CT scan or MRI or both. Conclusion: Both the Nexus and CCR guidelines act as a good guiding light in deciding about the need for the cervical spine X-ray in the emergency setup. Both guidelines are effective in ruling out cervical spine injuries in the majority of cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".