Multi-modal assessment of outcomes in pediatric mild traumatic brain injury
Notice bibliographique
Résumé
Objective: Mild traumatic brain injury is the most common type of head injury among children and adolescents. Symptoms are highly heterogeneous and are affected by several factors pre- and post-injury, making diagnosis and symptom management challenging. This study aims to look at using multi-modal analysis to determine relationships between symptoms and contribute to the understanding of how acute symptoms transition to a chronic pathology. Study design: Participants aged 8-18 years and with a medically diagnosed mTBI were recruited through the Alberta Children’s Hospital Brain Injury Clinic. Age- and sex-matched healthy controls were recruited through word of mouth, sibling matches, and the Healthy Infants and Children's Clinical Research Program (HICCUP). Participants were categorized as follows: 1) symptomatic mTBI (n=26), 2) repetitive mTBI (n=14), and controls (n=27). Study appointments occurred within 6-16 weeks from the initial injury where the following assessments were administered: symptom assessment, balance assessment, neuropsychological evaluation, ERP assessment, neuroimaging, and blood collection for cytokine analysis. Each assessment was analyzed individually, and then used to build a multi-modal elastic-net regression model to identify significant predictors among all of the outcomes. Results: mTBI participants had higher PCSI scores and lower PedsQL scores compared to controls, indicating higher symptom burden. The mTBI groups had more difficulties with school, as reflected by the BASC assessment. No differences were found between groups for the balance assessment or the ERP assessment. On the CNSVS test, female mTBI participants had lower Neurocognition Index scores and faster reaction times compared to female controls. Repetitive mTBI males had faster psychomotor speed compared to symptomatic mTBI males. CTACK levels were elevated in female mTBI groups, SCGF-B levels were lower in male mTBI groups, and MDC levels were lower in female mTBI groups when compared to controls. No differences in FA and MD scores were found between groups and sexes scores for the left and right CPC tracts. Multi-modal assessment revealed important model predictors from the neuropsychology and cytokine assay modalities. Conclusion: Multi-modal assessment is a necessary tool to understand the key factors involved in the secondary injury and symptoms that arise after a pediatric mTBI. It is more informative than looking at individual assessments and highlights the limitations of looking at specific biomarkers in isolation when studying a complex injury affecting multiple systems in the body.
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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| 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 | 0,001 |
| 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 ».