Can we manage sport related concussion in children the same as in adults?: Figure 1
Bibliographic record
Abstract
Evidence based guidelines are required for the management of concussive injury in children Consensus guidelines for managing sport related concussion in adults have been increasingly widely implemented.1 So far, there are no guidelines that enable clinicians to manage similar sporting concussive injuries in children. Furthermore, there are a number of important anatomical, physiological, and behavioural differences between adults and children that suggest that adult guidelines will need to be either modified or rewritten to manage injuries in this age group. The annual incidence of traumatic brain injury (TBI) in adults is remarkably constant worldwide and has been estimated at between 180 and 300 cases per 100 000 population.2–5 This is believed to be an underestimate of the true incidence as an equivalent number of mild injuries are treated by general practitioners and do not result in hospital admission.6 Direct sport participation accounts for approximately 15–20% of all such TBI3,7 and in children a further smaller percentage of TBI is associated with play activities.8 In children aged 15 years and under, the estimated incidence rate of TBI is 180 per 100 000 children per year of which approximately 85% are categorised as mild injuries.7 In the US, it has been estimated that more than 1 million children sustain a TBI annually and that TBI accounts for more than 250 000 paediatric hospital admissions as well as more than 10% of all visits to emergency service settings.9 In child and adolescent populations, few well controlled studies exist to identify the age specific frequency and outcome of sport related concussive injuries. The most common cognitive sequelae of concussive injuries in children are the same as for adults, namely reduced speed of information processing, poor attention, and impaired executive function.10–14 Concussion may also have a significant …
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.037 |
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".