The evaluation and management of acute concussion differs in young children
Bibliographic record
Abstract
BACKGROUND: There are many reasons why concussion in children needs to be considered different from adults. The Zurich (2008) recommendations on the management of concussion in children are restricted to children less than to 10 years of age. It does not include recommendations for children aged 5-10 years. The aim of this study is to review the current literature on (1) concussion assessment at the sideline and during recovery stages, especially in the age group 5-15 years, and (2) the management of concussion in children and adolescents. METHODS: A literature review using the MEDLINE database was undertaken. Articles were selected that included evaluation and/or management in children aged 5-15 years. RESULTS: There are no sideline assessment tools validated for use in this age group. There are a number of different symptom scales that have been validated during different stages of the follow-up assessment in children. No single paediatric concussion assessment tool has been validated for use from sideline through to all stages of recovery. Reliability studies have been published on Balance Error Scoring System in children, but validity studies in this age group have not been published. The management of concussion includes withdrawal from play on the day and cognitive and physical rest. The priority of concussion management in children is to return to learn; while this is usually rapid, there are some children in whom a graduated return to school is required, which should include a number of accommodations. CONCLUSIONS: A young child is physically, cognitively and emotionally very different from adults, and requires the use of a different set of tools for the diagnosis, recovery-assessment and management of concussion. Age-specific, validated diagnostic tools are required, and management of concussion in children should focus attention on return to learn before considering return to play.
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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 | 0.010 |
| 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 | 0.001 |
| 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 | 0.001 |
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".