Comparative study of the Three Criteria NEXUS II (National Emergency X-Ray Utilization Study) Canadian Computed Tomography Head Rule (CCHR) and American College of Emergency Physicians (ACEP) in the Diagnosis of Minor Head Trauma
Bibliographic record
Abstract
Background and Objective: Disabilities caused by traumatic brain injuries affect millions of people worldwide, the use of CT scan (CT) is a method for the diagnosis of lesions.The aim of this study was to compare the criteria of sensitivity and specificity in determining the necessity of CT. Materials and Methods:In this cross-sectional study, 100 traumatic patients who referred to the emergency department of Kosar Hospital with head injury were enrolled.On the basis of clinical examination and medical record for each system (ACEP, CCHR, Nexus) related clinical items recorded, and based on which scores were calculated for each.Data were analyzed using descriptive statistics, sensitivity and specificity, Chi-square, ROC curve, SPSS software version 20.Results: Results showed that, in terms of gender, 47 (47%) were male and 53 (53%) were female.The mean age of patients was 46.02 ± 18.20 years.37% had a head hematoma and 50% had a trauma.The sensitivity and specificity of the three criteria used (ACEP, Nexus and CCHR) were: 29.40% -62.50%, 13.3% -96.8% and 38.23% -71.87%, respectively.Significance was observed between sensitivity and specificity of the three criteria (p <0.001). Conclusion:The results of the sensitivity and specificity of the study were inconsistent with the findings of other studies.The proposed criteria of this study for CT may not have acceptable sensitivity and specificity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".