Applicability of CT examination decision rules in head injured children
Bibliographic record
Abstract
Objective To explore the applicability of the three commonly used CT examination decision rules in Chinese head injured children. Methods This prospective observational study included 1 538 children and adolescents (aged <18 years), who were treated at the Emergency Department of First Hospital of Shanxi Medical University after head injuries. The three clinical decision rules include the Children’s Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE; UK); the prediction rule for the identification of children at very low risk of clinically important traumatic brain injury, that was developed by the Pediatric Emergency Care Applied Research Network (PECARN; USA), and the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) rule. Diagnostic accuracy had been evaluated by using the rule-specific predictor variables to predict each rule-specific outcome measure in populations who met inclusion and exclusion criteria for each rule. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and ROC curve were referred to the diagnostic accuracy. Indicators were characterized by 95% CI. Results Of the 1 538 patients, CTs were obtained for 339 patients (22.04%). Forty-nine patients (3.19%) had positive CT results, 8 patients (0.52%) underwent neurosurgery, 2 patients (0.13%) died, and 1 patient (0.07%) may be missed. In this study, CHALICE was applied for 1 394 children (90.70%; 95% CI: 89.24%-92.15%), PECARN for 801 children (52.11%; 95% CI: 49.62%-54.61%), and CATCH for 325 patients (21.15%; 95% CI: 19.10%-23.19%). The validation sensitivities of CHALICE, PECARN, and CATCH rules were 92.6% (74.2%-98.7%), 100% (56.1%-100%), and 85.7% (42.0%-99.2%), respectively; the specificities were 78.1% (75.7%-80.2%), 48.0% (44.5%-51.5%) and 70.8% (65.4%-75.6%); positive predictive value were 7.7% (5.1%-11.3%), 0.9% (0.4%-1.9%) and 6.1% (2.5%-13.2%); and negative predictive value were 99.8% (99.2%-100%), 99.1% (98.1%-99.6%), and 99.6% (97.2%-100%), respectively. Conclusions The clinical decision rules of CHALICE, PECARN and CATCH have high sensitivities. The specificity of PECARN rule is lower than those of CHALICE and CATCH rules. The above three clinical decision rules can be used for the decision of CT examination in Chinese children with head injury in practice. Key words: Children; Head trauma; CT examination; Clinical decision rules; Applicability; CATCH; PECARN; CHALICE
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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 | 0.004 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".