1 Predictors of Neurocognitive Outcome in Pediatric Ischemic and Hemorrhagic Stroke
Notice bibliographique
Résumé
Objective: Neurocognitive deficits commonly occur following pediatric stroke and can impact many neuropsychological domains. Despite awareness of these deleterious effects, neurocognitive outcome after pediatric stroke, especially hemorrhagic stroke, is understudied. This clinical study aimed to elucidate the impact of eight factors identified in the scientific literature as possible predictors of neurocognitive outcome following pediatric stroke: age at stroke, stroke type (i.e., ischemic vs. hemorrhagic), lesion size, lesion location (i.e., brain region, structures impacted, and laterality), time since stroke, neurologic severity, seizures post-stroke, and socioeconomic status. Participants and Methods: Ninety-two patients, ages six to 25 and with a history of pediatric stroke, chose to participate in the study and were administered standardized neuropsychological tests assessing verbal reasoning, abstract reasoning, working memory, processing speed, attention, learning ability, long-term memory, and visuomotor integration. A standardized parent questionnaire provided an estimate of executive functioning. Statistical analyses included spline regressions to examine the impact of age at stroke and lesion size, further divided by stroke type; a series of one-way analysis of variance to examine differences in variables with three levels; Welch’s t-tests to examine dichotomous variables; and simple linear regressions for continuous variables. Results: Lesion size, stroke type, age at stroke, and socioeconomic status were identified as predictors of neurocognitive outcome in our sample. Large lesions were associated with worse neurocognitive outcomes compared to small to medium lesions across neurocognitive domains. Exploratory spline regressions suggested that ischemic stroke was associated with worse neurocognitive outcomes than hemorrhagic stroke. Based on patterns shown in graphs, age at stroke appeared to have an impact on outcome depending on the neurocognitive domain and stroke type, with U-shaped trends suggesting worse outcome across most domains when stroke occurred at approximately 5 to 10 years of age. Socioeconomic status positively predicted outcomes across most neurocognitive domains. Participants with seizures had more severe executive functioning impairments than youth without seizures. Youth with combined cortical-subcortical lesions scored lower on abstract reasoning than youth with cortical and youth with subcortical lesions, and lower on attention than youth with cortical lesions. Neurologic severity predicted scores on abstract reasoning, attention, processing speed, and visuomotor integration, depending on stroke type. There was no evidence of differences on outcome measures based on time since stroke, lesion laterality, or lesion region defined as supra-versus infratentorial. Conclusions: The current study contributed to the scientific literature by identifying lesion size, stroke type, age at stroke, and socioeconomic status as predictors of neurocognitive outcome following pediatric stroke. Future research should examine other possible predictors of neurocognitive outcome that remain unexplored. Multisite collaborations would provide larger sample sizes and allow teams to build models with better statistical power and more predictors. Enhancing understanding of neurocognitive outcomes following pediatric stroke is a first step towards improving appraisals of prognosis. Findings are clinically applicable as they provide professionals with information that can help assess individual expected patterns of recovery and thus refer patients to appropriate support services.
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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 | 0,005 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».