The Concussion Challenge Assessment: Development and reliability of a novel gross motor assessment tool for paediatric concussion
Bibliographic record
Abstract
Aims: The aim of this study was to develop a gross motor performance clinical assessment tool, the Concussion Challenge Assessment (CCA), for paediatric concussion populations. Methods: An expert panel evaluated tasks from the Acquired Brain Injury Challenge Assessment to determine relevant tasks for a paediatric concussion population. These tasks were administered to a convenience sample of 854 healthy youth. An analysis of the response options for each task, considering task difficulty, was performed. The test-retest reliability of each task was considered to finalise the tool. Results: The Acquired Brain Injury Challenge Assessment was reduced to six tasks (three coordination, two speed and agility, and one strength) to create the CCA. Population-specific 4-point response options were generated, which, upon examination of task difficulty, were revised as 5-point response sets to better capture performance differences. The test-retest reliability results led to acceptance of all six: three performance tasks and three exertion tasks. Conclusion: This development of the CCA is an important step in creating a gross motor performance assessment tool that can assist in the determination of when youth are able to safely return to activity following a concussion.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".