Paediatric Head Injury and Traumatic Brain Injury.
Bibliographic record
Abstract
Aim To determine prevalence of head injury presenting to paediatric emergency departments (PEDs) and characterise by demographics, triage category, disposition neuroimaging or re-attendance. Methods Presentations in 2014 and 2015, with diagnoses of head injury, intracranial bleed, skull fracture including single or re-attendances within 28 days post head injury to all national PEDs, were analysed. Demographics, triage score, imaging rate, admission, mechanisms and representation rate were recorded. Results Head injury was diagnosed in 13,392 of 224,860 (5.9%), median (IQR) age 3.9 (1.4 - 8.3) years. Regionally 3% of children <5 years attend each year. The total admitted/transferred was 10.8% (n=1460). Neuroimaging rate was 4.3% (n= 570). Falls predominated. Sport accounted for 12.2%. Conclusion One in twenty children PED presentations are head injury, over half in preschool children. A sizeable number were symptomatic reflected by admission, transfer, imaging or re-attendance. Observational management was favoured over imaging reflected in the higher admission versus imaging rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".