19 Preseason Neurocognitive Test Performance and Symptom Reporting Among Student Athletes with Autism Spectrum Disorders
Notice bibliographique
Résumé
Objective: Participation in sports likely confers multiple benefits for children and adolescents with autism spectrum disorder (ASD). Adolescent student athletes often undergo preseason testing as part of a broader concussion management program for schools. This study compares preseason neurocognitive functioning and symptom reporting between high school athletes with and without ASD. Participants and Methods: Participants were derived from a database of 60,751 adolescent student athletes from Maine (aged 13-18) who completed preseason testing between 2009 and 2019 and did not have missing data on the history question relating to ASD. There were 425 students (0.7%) who self-reported having been diagnosed with ASD in their health history. Cognitive functioning was measured by ImPACT, and the Post-Concussion Symptom Scale (PCSS) was used to obtain symptom ratings. Group differences between the ASD and the population control group on the five ImPACT cognitive test composite raw scores and the total symptom score from the PCSS were examined using Mann-Whitney U tests. Results: Compared to the population control sample, those with ASD reported much greater rates of comorbid conditions: attention deficit/hyperactivity disorder (50.1% vs. 10.3%), special education (39.2% vs. 4.4%), learning disabilities (43.8% vs. 4.4%), and prior treatment for a psychiatric condition (23.4% vs. 7.5%). Groups differed significantly across all neurocognitive composites (p values <.002). However, all differences were negligible in terms of the magnitude of the effects (r values range from 0.01-0.03). The groups also differed significantly on the PCSS total symptom score (p<.001), but the magnitude of the difference was negligible (r=.031). Among boys, the ASD group endorsed 21 of the 22 symptoms at a greater rate. Among girls, the ASD group endorsed 11 of the 22 individual baseline symptoms at a greater rate than the control group. Examples of symptoms that were endorsed at a higher rate among both boys and girls with ASD: sensitivity to noise (girls: odds ratio, OR=4.38; boys: OR=4.99), numbness or tingling (girls: OR=3.67; boys: OR=3.25), difficulty remembering (girls: OR=2.01; boys: OR=2.49), difficulty concentrating (girls: OR=1.82; boys: OR=2.40), sleeping more than usual (girls: OR=1.94; boys: OR=1.97), sensitivity to light (girls: OR=1.82; boys: OR=1.76), sadness (girls: OR=1.72; boys: OR=2.56), nervousness (girls: OR=1.80; boys: OR=2.27), and feeling more emotional (girls: OR=1.79; boys: OR=2.84). Conclusions: Students with ASD participating in organized sports are likely high functioning, on average. There were small differences in their cognitive test scores compared to the population control sample. They endorsed more symptoms, however, during baseline preseason testing. If they sustain a concussion, their clinical management should be more intensive to maximize the likelihood of swift and favorable recovery.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».