Epidemiology of traumatic brain injury in children receiving intensive care in the UK
Bibliographic record
Abstract
AIMS: To describe the epidemiology of children with traumatic brain injury (TBI) admitted to paediatric intensive care units (PICUs) in the UK. METHODS: Prospective collection of clinical and demographic information from paediatric and adult intensive care units in the UK and Eire between February 2001 and August 2003. RESULTS: The UK prevalence rate for children (0-14 years) admitted to intensive care with TBI between February 2001 and August 2003 was 5.6 per 100,000 population per year (95% Poisson exact confidence intervals 5.17 to 6.05). Children admitted to PICUs with TBI were more deprived than the population as a whole (mean Townsend score for TBI admissions 1.19 v 0). The commonest mechanism of injury was a pedestrian accident (36%), most often occurring in children over 10. There was a significant summer peak in admissions in children under 10 years. Time of injury peaked in the late afternoon and early evening, a pattern that remained constant across the days of the week. Injuries involving motor vehicles have the highest mortality rates (23% of vehicle occupants, 12% of pedestrians) compared with cyclists (8%) and falls (3%). In two thirds of admissions (65%) TBI was an isolated injury. CONCLUSIONS: TBI in children requiring intensive care is more common in those from poorer backgrounds who have been involved in accidents as pedestrians. The summer peak in injury occurrence for 0-10 year olds and late afternoon timing give clear targets for community based injury prevention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".