The comparison of PECARN, CATCH, AND CHALICE criteria in children under the age of 18 years with minor head trauma in emergency department
Bibliographic record
Abstract
Introduction: Minor head traumas constitute a significant part of childhood injuries. The incidence of intracranial pathologies in children with minor head trauma varies in the range of 3%–5%, but it is higher among younger infants. The criteria of the Pediatric Emergency Care Applied Research Network, Canadian Assessment of Tomography for Childhood Head Injury, and Children's Head Injury Algorithm for the Prediction of Important Clinical Events are the most frequently accepted clinical decision‐making criteria that were developed for selective computerized tomography requests. This study was conducted to assess the diagnostic performances of the Pediatric Emergency Care Applied Research Network, Canadian Assessment of Tomography for Childhood Head Injury, and Children's Head Injury Algorithm for the Prediction of Important Clinical Events criteria in Turkish society, determine their validity, and find the most suitable algorithm for cranial imaging in children with minor head trauma. Methods: This study retrospectively examined the data of patients under the age of 18 years who were admitted to the Emergency Medicine Department of Uludağ University Medical Faculty due to minor head trauma; 530 patients were included as they complied with the criteria. The exclusion criteria were being any trauma patients above the age of 18 years, Glasgow Coma Scale <13, pregnant patients, hemorrhagic diathesis, using anticoagulants, patients with penetrant trauma, patients with priorly known brain tumor, and patients with neurological diseases. The patients were divided into group based on the Pediatric Emergency Care Applied Research Network, Canadian Assessment of Tomography for Childhood Head Injury, and Children's Head Injury Algorithm for the Prediction of Important Clinical Events Criteria. Results: Among all patients, 37.40% were female and 62.60% were male. Abnormal computed tomography findings such as epidural bleeding, subdural bleeding, and skull fractures were detected in 44 of the patients. The sensitivity of the Pediatric Emergency Care Applied Research Network criteria was 72.4%, the specificity was 54.5%, the sensitivity of the Canadian Assessment of Tomography for Childhood Head Injury criteria was 57.8%, the specificity was 50%, the sensitivity of the Children's Head Injury Algorithm for the Prediction of Important Clinical Events criteria was 87.7%, and the specificity was 20%. Conclusion: Given the populations to which the rules apply, it is understood that the Children's Head Injury Algorithm for the Prediction of Important Clinical Events criteria is more determinative in detecting pathological computed tomography outcomes compared to Pediatric Emergency Care Applied Research Network and Canadian Assessment of Tomography for Childhood Head Injury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".