The Spectrum of Seizure Disorders in Children at a Secondary Care Hospital in Saudi Arabia
Bibliographic record
Abstract
Objective: This study was conducted to find out the presentation, clinical features, diagnosis, and outcome of seizures in children in a secondary care hospital. Methodology: This was a prospective, cross-sectional analytical study conducted from September 2018 to April 2020 in a secondary care hospital in the Kingdom of Saudi Arabia. Children presented with seizures to the hospital were included and studied for the demographic profile, clinical presentation, diagnosis, and course of the disease. Results: A total of 73 cases were included in the study. Out of these, 48 (65.8%) were in the age group of 1-5 years, while 25 (34.2%) were in the age group of 6-14 years. Males were 33 (45.21), while females were 40 (54.8%). More than half of the cases were already diagnosed 43 (58.9). Generalized tonic-clonic seizures were the most common seizure type in the patients. The most common etiology of seizures was febrile seizure, followed by epilepsy. Conclusion: Seizures in children are not uncommon presentations with varying profiles in various regions of the world. Febrile seizures and epilepsy are the commonest cause of seizures in children. Most seizure disorders have a good prognosis, provided that a comprehensive approach is used for the management.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".