Child concussion recognition and recovery: a community delivered, evidenced-based solution
Bibliographic record
Abstract
Pediatric concussion is a growing health concern. Concussion is generally poorly understood within the community. Many parents are unaware of the signs and varying symptoms of concussion. Despite the existence of concussion management and return to play guidelines, few parents are aware of how to manage their child's recovery and return to activities. Digital health technology can improve the way this information is communicated to the community. A multidisciplinary team of pediatric concussion researchers and clinicians translated evidence-based, gold-standard guidelines and tools into a smartphone application with recognition and recovery components. HeadCheck is a community facing digital health application developed in Australia (not associated with HeadCheck Health) for management of concussion in children aged 5-18 years. The application consists of (I) a sideline concussion check and (II) symptom monitoring and symptom-targeted psychoeducation to assist the parent manage their child's safe return to school, exercise and sport. The application was tested with target end users as part of the development process. HeadCheck provides an accessible platform for disseminating best practice evidence. It provides feedback to help recognize a concussion and symptoms of more serious injuries and assists parents guide their child's recovery.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".