Clinical characteristics of pediatric epilepsy in Palestine: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Epilepsy, a prevalent neurological disorder in children, is characterized by recurrent unprovoked seizures and developmental challenges. Although pediatric epilepsy is well-studied globally, research in Palestine is limited, which affects targeted healthcare interventions. Furthermore, socioeconomic barriers and limited resources make it difficult to manage epilepsy locally. This study addresses the clinical, diagnostic, and therapeutic aspects of pediatric epilepsy in Palestine to inform evidence-based healthcare planning and improve outcomes for affected children. METHODS: A retrospective cross-sectional study was conducted between December 2023 and October 2024, analyzing medical records of 411 pediatric epilepsy patients (aged 2 months to 18 years) diagnosed between 2019 and 2024 at two major pediatric neurology clinics in the West Bank. The data on demographics, seizure types, diagnostic tools, etiology, comorbidities, and treatment approaches were collected retrospectively. The statistical analysis was conducted using IBM SPSS version 24, which employed both descriptive and analytical statistics to identify associations. RESULTS: Of the 411 patients, 67.2% were male, and 38.3% were school-age children at the time of seizure onset. Focal seizures were the most common type (62.8%). Among epilepsy syndromes, self-limited epilepsy with centrotemporal spikes (SeLECTs) was the most frequently identified (10.9%). The etiology was unknown in 53.8% of cases; genetic and structural causes were identified in 7.3% and 13.6%, respectively. Monotherapy was used in 62% of patients, including 71.6% of those without comorbidities. A statistically significant association was found between the number of antiseizure medications (ASMs) and the presence of comorbidities (p = 0.001), with patients without comorbidities more likely to receive monotherapy. Significant correlations were also identified between seizure type and age of onset (p = 0.000), etiology (p = 0.001), and type of comorbidities (excluding mood disorders). Drug-resistant epilepsy affected 3.4% of patients and was significantly associated with younger age at seizure onset (p = 0.042), cognitive and developmental comorbidities (p < 0.05), and genetic etiology (p = 0.02). Only 1% of patients received surgical intervention, and none were treated with ketogenic diets. CONCLUSION: This study focused on the clinical characteristics of pediatric epilepsy and highlighted the challenges associated with the availability of treatment modalities, advanced diagnostic tools such as genetic testing, and the limited use of dietary and surgical interventions in Palestine.
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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 | 0.001 | 0.001 |
| 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".