Citations

Abstract Objective Invasive presurgical evaluation plays a key role in pediatric epilepsy surgery, particularly in magnetic resonance imaging (MRI)‐negative cases, by guiding resective, disconnective, or ablative procedures. This International League Against Epilepsy (ILAE) Pediatric Epilepsy Surgery Taskforce study provides an updated global overview of current invasive evaluation practices. Methods Group‐level data were collected from 61 epilepsy surgery programs (49 pediatric‐only) in 29 countries across six continents. Included were children and adolescents who underwent presurgical evaluation and epilepsy surgery in 2023. The study was designed to enable comparison with the similar ILAE survey conducted in 2004. Results A total of 2427 patients were included. Invasive evaluations were performed in 21.1% of cases, most frequently in North America (33.7%, higher than Europe: 18.0%, p = .003). Among invasive cases, 32.3% had no detectable MRI abnormalities. The main indication for invasive evaluation was seizure onset localization (88.1%), followed by motor or sensory mapping (17.2%) and language mapping (14.2%). Stereoelectroencephalography (SEEG) was the predominant technique (19.8% overall, 93.6% of invasive cases), more common in North America (30.0%, p = .021) and less common in South America (7.4%, p < .001). Subdural electrodes were used in only 3.2% of invasive cases, and combined depth and subdural approaches in 3.2%. SEEG‐guided radiofrequency thermocoagulation (RF‐TC) was performed in 40.9% of SEEG cases, most commonly in Asia (63.8%). In 16.2% of invasive evaluations, patients did not proceed to resection, disconnection, or ablation, with the highest rate in Europe (28.5%). Significance This global survey provides the first broad overview of invasive evaluation practices in pediatric epilepsy surgery across participating centers worldwide. It highlights the widespread adoption of SEEG, declining use of subdural electrodes, and increasing application of SEEG‐guided RF‐TC. The high proportion of MRI‐negative cases and the considerable proportion of patients not proceeding to resection, disconnection, or ablation underscore the complexity of contemporary surgical candidates and the need for further refinement of selection strategies.

Abstract Objective Pediatric epilepsy surgery is well established, but contemporary global data on referral and presurgical evaluation practices are lacking. This International League Against Epilepsy (ILAE) Pediatric Epilepsy Surgery Task Force study provides an updated overview of current trends and regional differences. Methods Group‐level data were collected from 61 epilepsy surgery programs (49 pediatric‐only) across 29 countries and six continents, identified through ILAE networks, and included all children and adolescents treated in 2023 who underwent presurgical evaluation/epilepsy surgery. Results Group‐level data were available for 2427 patients. Mean age at surgery was 9.1 ± 4.9 years; mean epilepsy duration was 5.3. At surgery, 3.2% were <1 year old (highest in Oceania: 5.1%), and 6.1% were nonpharmacoresistant (highest in Europe: 15.0%). Prior neurosurgery was reported in 14.2% (highest in North America: 28.8%), including 8.0% resections (6.1% for epilepsy, 1.5% for tumors; highest in Oceania: 16.5%), 2.3% disconnections (1.3% corpus callosotomy; highest in South America: 4.7%), and 4.2% neuromodulation (3.7% vagal nerve stimulation, .2% responsive neurostimulation, one deep brain stimulation; highest in North America: 12.2%). Developmental and epileptic encephalopathies (DEEs) at surgery included Lennox–Gastaut syndrome (7.4%), infantile epileptic spasms syndrome (5.1%), and DEE with spike–wave activation in sleep (1.5%). Presurgical investigations included fluorodeoxyglucose positron emission tomography (52.6%; highest in Oceania: 79.7%), genetic testing (46.8%; highest in Asia: 54.3%), magnetic resonance imaging (MRI) postprocessing (32.4%; highest in South America: 53.0%), functional MRI (fMRI; 15.2%; highest in North America: 40.3%), magnetoencephalography (11.9%; highest in North America: 39.3%), single photon emission computed tomography (9.6%; highest in North America: 22.2%), high‐density electroencephalography (EEG; 1.9%; highest in Europe: 4.7%), source localization (1.6%; highest in Oceania: 7.6%), Wada test (1.2%; highest in North America: 3.5%), and EEG‐fMRI (.5%; highest in Europe: 1.1%). Significance Despite some early surgeries, including in infancy and before pharmacoresistance, mean epilepsy duration before surgery remains >5 years. Reoperations are common, with resection more frequent than neuromodulation. Genetic testing in nearly half of patients reflects its growing relevance, and the high rate of DEEs underscores the complexity of surgical candidates.

Abstract Objectives The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs—a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. Materials and Methods We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. Results The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. Discussion This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. Conclusions The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.