The validity of canadian cervical spine rules and the NEXUS low risk criteria in trauma patients
Bibliographic record
Abstract
Background: The first decision rule developed to identify clinically significant injuries in blunt cervical trauma patients is National Emergency X-Radiography Utilisation Study (NEXUS). In the NEXUS study, the negative predictive value (NPV) has been determined as 99.8%. Sensitivity of Canadian Cervical Spine Rules (CCR) was reported as 99.4%, specificity as 45.1% and NPV was reported as 100%. The objective of this study is to determine the reliability and utility of NEXUS and CCR for Turkish patient population that has a risk of cervical injury. Methods: This prospective observational study included 225 patients, all stable, conscious patients over 16 years of age who had acute trauma and were brought to the hospital with ambulances or using their own means and who had been injured by a mechanism that may cause cervical trauma, and without exclusion criteria. The patients included in the study were then evaluated for NEXUS and CCR validity.Results: When CCR was evaluated as a whole, it was determined that all pathological cases were identified using these rules. In terms of identifying the presence of pathological imaging finding the sensitivity of CCR was 100% (95% CI % 56-100) and specificity was 3.2% (95% CI 1.4-6.7%). NEXUS's sensitivity was calculated as 93% (95% CI 83-97) and specificity as 1.3%(95% CI 0.2-5.1). Conclusion: CCR and the NEXUS were determined to be useful in the emergency department for the exclusion of cervical pathologies. CCR were more reliable and useful when compared with the NEXUS.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".