Telehealth for Children With Epilepsy Is Effective and Reduces Anxiety Independent of Healthcare Setting
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".