Evaluation of Three Clinical Decision Rules in Pediatric Patients with Minor Head Injury: PECARN, CHALICE and CHATCH
Bibliographic record
Abstract
Objective: In this study, we aimed to evaluate the diagnostic accuracy of the Pediatric Emergency Care Applied Research Network (PECARN), Canadian Assessment of Tomography for Childhood Head Injury (CATCH), and children's head injury algorithm for the prediction of important clinical events guidelines in identifying clinically important traumatic brain injury (ciTBI) in pediatric patients with minor head injury. Materials and Methods:This single-center, prospectively designed study was performed in the emergency department (ED) of a tertiary hospital.The study included patients under 18 years old who presented to the ED with head trauma and a GCS of 14-15.The primary outcome of the study was the relationship between the decision rules and ciTBI.Results: The study was completed with 502 patients.It was found that the PECARN algorithm was 80% sensitive in detecting ciTBI in patients younger than 2 years of age, and 84.55% in patients aged 2 years or older.While this rate decreased (50.0%) in CATCH, it was higher (89.54%) in CHALICE.In the detection of patients without a risk (specificity), all 3 algorithms found good detections, and the specificity rates were between 82% and 90%.Conclusion: ciTBI risk prediction models will assist in clinical decision making and establish an accurate neuroimaging strategy.According to the results of our study, all three clinical decision rules can be safely used in the management of pediatric minor head trauma patients.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".