Tracking developmental trajectories on neurocognitive testing in young athletes ages 5–12
Bibliographic record
Abstract
Objective Despite increased attention to sports-related concussion, there is a lack of research on younger athletes. The purpose of this study was to establish developmental trajectories on neurocognitive testing in younger athletes ages 5 to 12. Design Pre-Test Only Design. Setting Multi-site study from numerous locations in the Eastern USA and Canada. Participants A total of 788 youth athletes, 73% male, assigned to groups on the basis of age: Ages 5–6 (N=128), Ages 7–8 (N=282), Ages 9–10 (N=284), Ages 11–12 (N=94). Intervention An iPad-based Paediatric version of the ImPACT test was administered individually to participants in a quiet office setting. Outcome measures Accuracy scores were documented on: Word List Learning (Immediate, Delayed, Recognition); and accuracy and timing scores on Design Rotation, Choice Reaction Time, Visual Sequencing and Visual Memory. Main results One-way ANOVAs with Bonferroni correction for multiple comparisons, and Scheffé post-hoc comparisons were conducted between age groups on the dependent measures. All measures showed statistically significant changes (p< 0.001) across the developmental age groups, with better performance in older participants and lower performance in younger participants. Conclusions Youth athletes between the ages of 5 and 12 are underrepresented with respect to age-appropriate assessment measures, and these results document the need for developmentally- and age-appropriate measures and normative data, in order to capture neurocognitive change across developmental stages. Competing interests Dr. Schatz serves on the ImPACT Scientific Advisory Board. None.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".