Characteristics of Intracranial Injuries in Pediatric Patients Following Blunt Head Trauma
Bibliographic record
Abstract
OBJECTIVES: Pediatric head trauma is a frequent reason for presentation to the emergency department. Despite this, there are few reports on specific characteristics and injury patterns in head injured children. The goal of this study was to evaluate head injury patterns in children with blunt head injury and their prevalence by age group. METHODS: This is a planned secondary analysis of the NEXUS II Head CT validation study. Consecutive patients with blunt head trauma were enrolled between 2006 and 2015. Demographics and criteria from 2 clinical decision instruments (NEXUS and Canadian Head CT rules) were gathered at the time of enrollment. We abstracted and cataloged injuries for pediatric patients based on radiologist report. Frequencies of injuries and severity were analyzed by developmental age group. RESULTS: A total of 1018 pediatric patients were enrolled, 128 (12.6%) of whom had an injury on computed tomography scan. Median age was 11.9 (Interquartile range 4.5-15.5) for all patients and 12 (4.8-15.5) for injured patients. Of injured patients, 49 (38.3%) had a significant injury, and 27 (21.1%) received an intervention. Teenagers had the highest rate of significant injury (50%) and intervention (30%). Injuries were most frequently noted in the temporal (46.1%), frontal (45.3%), and parietal (45.3%) regions. Subarachnoid hemorrhage (29.7%) and subdural hematoma (28.9%) were the most common injuries observed.Intraparenchymal hemorrhage and cerebral edema were more prevalent in older age groups. The most common injury mechanism overall was fall from height (24.7%). Motor vehicle accidents and nonmotorized wheeled vehicle accidents were more common in older patients. CONCLUSIONS: Serious injuries requiring intervention were rarely encountered in pediatric patients experiencing blunt head trauma. Mechanisms of injury, type of injury, and rates of intervention varied between developmental age groups.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".