A global perspective on transitioning from pediatric to adult care in epilepsy
Bibliographic record
Abstract
OBJECTIVE: Transition planning in epilepsy is crucial to ensure continuity of care, particularly for adolescents with complex needs, yet global practices remain undefined. The International League Against Epilepsy Transition Task Force (ILAE TTF) aimed to evaluate worldwide practices, barriers, and provider perspectives on transitioning patients with epilepsy from pediatric to adult healthcare systems. METHODS: A cross-sectional, web-based survey was conducted between August 2021 and March 2024. The 50-item survey, adapted from validated instruments, was distributed in eight languages through ILAE chapters to health care professionals involved in the care of individuals with epilepsy. This study sought to evaluate the availability of structured transition programs, educational and systemic barriers, and recommendations to improve transition. Descriptive statistics, Fisher's exact tests, and multivariate logistic regression analyses were used to compare responses between adult and child neurologists, as well as by country region and income level. RESULTS: A total of 316 neurologists from 58 countries completed the survey. Only 9% reported structured transition programs both locally and nationally, whereas 59% reported none or were unaware of any. Respondents from the Global South were significantly less likely to report transition programs (odds ratio [OR], 0.16; 95% confidence interval [CI], 0.94-0.27; p < .001). Half of the participants believed pediatric teams should continue follow-up after transfer. Major barriers included a lack of financial support, limited transition-specific training, and adult neurologists unfamiliar with childhood-onset epilepsies. Reported patient-educational gaps included vocational guidance (73%), reproductive health (60%), and driving (57%). Recommendations to improve transition included integrating transition training into neurology curricula (87%), creating dedicated clinical structures and care networks (87%), establishing joint consultations between pediatric and adult neurologists (86%), and implementing national guidelines (89%). SIGNIFICANCE: This study demonstrates that epilepsy transition practices remain fragmented and underdeveloped worldwide, especially in low-resource settings. Enhanced training, improved clinical infrastructure, and better policy coordination are crucial to facilitate effective and equitable transitions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".