Invalidity Rates of Baseline ImPACT Concussion Assessments in High School Athletes with Disabilities
Bibliographic record
Abstract
Invalidity Rates of Baseline ImPACT Concussion Assessments in High School Athletes with Disabilities Introduction The Immediate Postconcussion Assessment and Cognitive Testing (ImPACT) is the most common computerized concussion assessment tool (Dessy, 2017). Previous studies showed higher rates of invalid baselines in individuals with autism, learning disabilities (LD) and Attention-Deficit/Hyperactivity Disorder (ADHD) (Maietta, 2021). Characteristics of these disabilities can resemble concussion symptoms which may complicate the evaluation process and diagnosis of concussion, a health concern in athletes (Harmon, 2013). This study aims to investigate invalidity rates of baseline ImPACT assessments among disability groups in high school athletes to explore its use in this population. Methodology This retrospective study analysed ImPACT (versions 4.0 & up) baseline assessments between 2021-2023 from 62 high schools in Hawai'i. Participants were divided into groups based on self-reported disability: No disability, LD, ADHD, dyslexia, autism and accompanying neurodevelopmental disorders (ND), dyslexia and ADHD, LD with ADHD and dyslexia. ImPACT software automatically flags invalid assessments upon completion. Invalidity rates were calculated for each disability group and a chi-squared(χ²) goodness of fit test was completed to determine invalidity rates(%) between disability groups. Results 25,275 participants (female=9,926, male=15,349) aged 15.35±1.14 were included. χ² analysis revealed significant association between invalid baselines and disability groups [χ² (7, n=25275) =70.753, p<.001]. Overall invalidity rate for all athletes was 7.15%. Individuals with LD (13.71%) and autism with accompanying ND’s (13.1%) had the highest rate of invalid baseline tests across groups. Conclusions Athletes with self-reported disabilities and ND’s elicit higher rates of invalid ImPACT baseline assessments potentially impacting healthcare. Instruments with increased sensitivity should be validated for use in individuals with disabilities. References Dessy, A. M., Yuk, F. J., Maniya, A. Y., Gometz, A., Rasouli, J. J., Lovell, M. R., & Choudhri, T. F. (2017). Review of assessment scales for diagnosing and monitoring sports-related concussion. Cureus, 9(12), e1922. Harmon, K. G., Drezner, J., Gammons, M., Guskiewicz, K., Halstead, M., Herring, S., Kutcher, J., Pana, A., Putukian, M., Roberts, W., & American Medical Society for Sports Medicine (2013). American Medical Society for Sports Medicine position statement: concussion in sport. Clinical journal of sport medicine: official journal of the Canadian Academy of Sport Medicine, 23(1), 1–18. Maietta, J. E., Barchard, K. A., Kuwabara, H. C., Donohue, B. D., Ross, S. R., Kinsora, T. F., & Allen, D. N. (2021). Influence of special education, ADHD, autism, and learning disorders on ImPACT validity scores in high school athletes. Journal
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".