Pediatric traumatic brain injury in a high-income developing country: experience at a level 1 neuro-trauma center
Bibliographic record
Abstract
BACKGROUND: Data on the incidence, prevalence and mortality of pediatric traumatic brain injuries (TBIs) in developing countries are not readily available or do not exist. AIM: The aim of this study was to study the epidemiology of pediatric TBI in developing countries. METHODS: A retrospective study was conducted in a high-volume Neurosurgery Department where we reviewed pediatric cases presenting with TBI between January 2015 and December 2019. Data were collected from the electronic medical records including the patients' demographics, neuro-vital signs, mechanism of TBI and treatment types. Radiological images were screened, and patients were classified according to the type of intracranial hemorrhage. The patient's outcome and Glasgow Coma Scale on discharge were also recorded. RESULT: Nine hundred and eighty-five cases with TBI were admitted over the period of 5 years. The average age was 53.3 months standard deviation (SD) of 39.4. Male gender accounted for 63.7% of the cases. The most common mechanisms of injuries were falls and road traffic accidents/motor vehicle collisions (63.3%, 18.3%), respectively. Nausea and vomiting followed by altered consciousness and drowsiness were the commonest presenting symptoms. Mild TBI accounted for 85.2% of the cases and the majority (92.08%) were treated conservatively (P < 0.005). 93.3% of the cases were categorized as mild head injury upon discharge. The mortality rate was 1.6% in severe TBI cases. CONCLUSION: Children less than 4 years of age were highly affected by TBI. This study gives emergency physicians and neurosurgeons in developing countries an expectation about TBI in pediatric cases and the immediate management to prevent further complications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".