Global Epidemiology of Pediatric Traumatic Brain Injury: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Traumatic brain injury (TBI) is a public health concern that leads to premature deaths and disability in a large number of children worldwide. The aim of this study was to estimate the global incidence of pediatric TBI and to compare the clinical presentation and outcome of pediatric TBI in different countries, regions, and income settings. METHODS: A systematic review was conducted to identify studies on pediatric patients with TBI published from 2000 onward. Studies that contained population-level data were included in meta-analyses to calculate the incidence of TBI and TBI-related mortality. Studies that reported hospital-level data were pooled to calculate mortality rates, proportion of patients with severe TBI, and proportion of TBI resulting from falls and road traffic injuries. RESULTS: The estimated global incidence of pediatric TBI was 226.4 per 100 000 children annually, using data from 25 studies in 5 regions. An estimated 1.9 per 100 000 children die from TBI each year. Based on 79 studies from 42 countries, 11.0% (median: 10.7%) of patients had severe TBI. The overall mortality rate was 1.4% (median: 3.2%). In 28 studies, the median mortality rate for severe TBI was 26.7%. Studies from low-income and middle-income countries reported higher proportions of patients with severe TBI and higher mortality rates. Road traffic injuries were the most common cause of TBI among children in Africa and in low-income countries. Falls were more common in other regions. CONCLUSION: Every year, about 5.4 million children worldwide sustain a TBI. Disparities in the estimated incidence rates of pediatric TBI among countries may represent true differences in frequency or variability in the capacity of health systems to address TBI. Population-level data remain lacking in low-income and lower-middle-income countries.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".