External validation and comparison of the Pediatric Emergency Care Applied Research Network and Canadian Assessment of Tomography for Childhood Head Injury 2 clinical decision rules in children with minor blunt head trauma
Bibliographic record
Abstract
OBJECTIVE: Among the pediatric population with minor head trauma, it is difficult to determine an indication for the usage of brain computerized tomography (CT). Our study aims to compare the efficiency of the most commonly used clinical decision rules: the Pediatric Emergency Care Applied Research Network (PECARN) and Canadian Assessment of Tomography for Childhood Head Injury 2 (CATCH2). METHODS: This retrospective study investigated whether the PECARN and CATCH2 rules were applicable to Korean children with minor head trauma for reducing the use of brain CT imaging, while detecting intracranial pathology. RESULTS: Overall, 251 patients (0-5 years old) admitted to emergency rooms within 24 hours of injury were included between August 2015 to August 2018. The performance results are as follows: the PECARN and CATCH2 rules had a sensitivity of 80.00% (51.91%-95.67%) and 100% (78.20%-100.00%) with a specificity of 28.39% (22.73%-34.60%) and 15.25% (10.92%-20.49%), respectively; the negative predictive values were 98.58% and 100%, respectively. Overall, the CATCH2 rule was more successful than the PECARN rule in detecting intracranial pathology; however, there was no significant difference between them. Furthermore, the PECARN and CATCH2 rules lowered the rate of head CT imaging in our study group. CONCLUSION: Both the rules significantly lowered the rate of indicated brain CT. However, since the CATCH2 rule had higher sensitivity and negative predictive value than the PECARN rule, it is more appropriate to be used in emergency rooms for detecting intracranial pathology in children with minor head trauma.
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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 | 0.001 |
| 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".