Children and youth with non-traumatic brain injury: a population based perspective
Bibliographic record
Abstract
BACKGROUND: Children and youth with non-traumatic brain injury (nTBI) are often overlooked in regard to the need for post-injury health services. This study provided population-based data on their burden on healthcare services, including data by subtypes of nTBI, to provide the foundation for future research to inform resource allocation and healthcare planning for this population. METHODS: A retrospective cohort study design was used. Children and youth with nTBI in population-based healthcare data were identified using International Classification of Diseases Version 10 codes. The rate of nTBI episodes of care, demographic and clinical characteristics, and discharge destinations from acute care and by type of nTBI were identified. RESULTS: The rate of pediatric nTBI episodes of care was 82.3 per 100,000 (N = 17,977); the average stay in acute care was 13.4 days (SD = 25.6 days) and 35% were in intensive care units. Approximately 15% were transferred to another inpatient setting and 6% died in acute care. By subtypes of nTBI, the highest rates were among those with a diagnosis of toxic effect of substances (22.7 per 100,000), brain tumours (18.4 per 100,000), and meningitis (15.4 per 100,000). Clinical characteristics and discharge destinations from the acute care setting varied by subtype of nTBI; the proportion of patients that spent at least one day in intensive care units and the proportion discharged home ranged from 25.9% to 58.2% and from 50.6% to 76.4%, respectively. CONCLUSIONS: Children and youth with nTBI currently put an increased demand on the healthcare system. Active surveillance of and in-depth research on nTBI, including subtypes of nTBI, is needed to ensure that timely, appropriate, and targeted care is available for this pediatric population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".