Go zika go: estudo da viabilidade de carros de brinquedo motorizados modificados para a mobilidade de crianças com Síndrome Congênita do Zika (SCZ)
Notice bibliographique
Résumé
Introduction: The clinical characteristics of Congenital Zika Syndrome (CZS) include a series of impairments and delays, mainly in cognitive and motor aspects, with a similar picture to Cerebral Palsy (CP). The prognosis of the motor performance of children with SCZ raises questions about the possibilities of these children's participation in everyday life. The literature points out that motorized mobility is a viable and effective possibility of intervention for children with motor disabilities, with positive impacts on general development, independent mobility, bodily functions, activities and participation. The Go Zika Go project was created to provide children with SCZ with an intervention model focused on participation results, using modified motorized toy cars, seeking to test the feasibility of this intervention. Objective: To explore the feasibility of a motorized mobility intervention for children diagnosed with SCZ without a prognosis for ambulation, including acceptability and preliminary efficacy. Materials and Methods: This is a feasibility study with a longitudinal design before and after the intervention, carried out at the Clínica Escola de Fisioterapia of the Faculdade de Ciências da Saúde do Trairi (Facisa/UFRN) with four children diagnosed with CZS. For this study, adherence, measured by attendance at intervention sessions, satisfaction, measured by the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) and Satisfaction Perception Questionnaire tools, and mobility learning, classified as by the Assessment of Learning Powered Mobility (ALP). Secondary outcomes related to the effect of the intervention were goal achievement assessed using the Goal Attainment Scaling (GAS), mobility and social function using the Pediatric Evaluation of Disability Inventory – Computer Adaptive-test (PEDI-CAT), and participation through the Young Children's Participation and Environment Measure (YC-PEM) or Participation and Environment Measure for Children and Youth (PEM-CY). The intervention with the modified cars lasted 12 weeks of training and 4 weeks of follow-up, with a frequency of three times a week and a dosage of 40 minutes. Descriptive statistical analyzes were performed for sociodemographic data, children's motor classification, intervention feasibility data (adherence, satisfaction data and learning with ALP), GAS, YCPEM/PEM-CY and PEDI-CAT. We also summarize the feasibility variables (recruitment, adherence, evaluation, safety and intervention costs) by relative and absolute frequencies. To explore the effects of the intervention, on the PEDI-CAT (mobility and social/cognitive), and the YC-PEM/PEM-CY data, the Wilcoxon test was applied comparing the changes between week 0 before the intervention, and sixteen weeks after the intervention, standard error measures were also used to verify changes in the PEDI-CAT domains. Results: Median age of children with SCZ included in the study was 4.75 years, two females and two males, 3 classified using the Gross Motor Function Classification System (GMFCS) as level V and one as level V. I The results showed adherence of 75% of the total intervention time, satisfied or very satisfied family members, and gains in learning how to use cars after the intervention, indicating that the Go Zika Go intervention is feasible. There was also an increase in the scope of the targets established based on the GAS. Changes in PEDICAT medians and participation outcomes were not statistically significant. Individual changes were perceived by the standard error analyzes in the mobility and social/cognitive domains. Conclusion: Intervention with modified toy cars proved to be feasible to provide children with SCZ with goal achievement, satisfaction and learning to use the modified car. We suggest the development of clinical trials to explore the effect of the intervention on the functional gains and participation of children with SCZ.
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 | 0,005 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 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 ».