Telehealth for Children With Epilepsy Is Effective and Reduces Anxiety Independent of Healthcare Setting
Notice bibliographique
Résumé
Objectives: The use of telemedicine has grown exponentially as an alternative to providing care to patients with epilepsy during the pandemic. We investigated the impact of the current pandemic among children with epilepsy from two distinct pediatric epilepsy centers. We also compared perceptions among those who received telemedicine against those who did not. Methods: We developed a questionnaire and invited families followed in Freiburg, Germany, and Calgary, Alberta, Canada, to participate during the initial 9 months of the pandemic. The survey contained 32 questions, 10 of which were stratified according to telemedicine exposure. Results: One hundred twenty-six families (80 in Freiburg, 46 in Calgary) participated, and 40.3% received telemedicine care. Most children (mean age 10.4 years, SD 5.1) had chronic epilepsy but poorly controlled seizures. Negative impacts were reported by 36 and 65% of families who had to reschedule appointments for visits and diagnostics, respectively. Nearly two-thirds of families reported no change in seizure frequency, while 18.2% reported either worsening or improvement of seizures. Although most families did not note behavioral changes, 28.2% reported behavior worsening. Families who received telemedicine care had a statistically significant reduction of parental self-reported anxiety level after virtual visits compared to those who did not experience telemedicine. Families with telemedicine consultations were more likely to consider future virtual care (84 vs. 65.2% of those without), even after the pandemic. Patient data safety, easy access to specialized services, and consistency with the same healthcare provider were graded as important in both centers, while a shorter waiting time was most relevant in Calgary. Conclusion: In our cohort, some children with epilepsy experienced increased seizures and worsening behavior during the first 9 months of the current pandemic. In addition, our data suggest that telemedicine might reduce parental anxiety symptoms, and families who experienced telehealth were more positive and open to similar appointments in the future.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 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 tête enseignante, 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 ».