Diagnostic evaluation of patients with epileptic spasms in the era of next‐generation sequencing
Bibliographic record
Abstract
OBJECTIVE: Epileptic spasms (ES) can be caused by a variety of etiologies. However, in almost half of cases, the etiology is unidentified. With the advent of next-generation sequencing (NGS), the recognition of genetic etiologies has increased. METHODS: We retrospectively reviewed the medical records of patients with ES who were evaluated in the comprehensive epilepsy program at King Fahad Specialist Hospital Dammam between 2009 and 2022. RESULTS: Our data show that in 57.7% of patients with ES, the etiology was unidentified after a standard clinical evaluation and neuroimaging. Of these patients, n = 25 (35.2%) received a genetic diagnosis after some form of genetic testing, and 3.1% of patients from specialized metabolic work indicated the need for genetic testing to confirm the diagnosis. Karyotyping led to a diagnosis in 3.6% of patients, and chromosomal microarray led to a diagnosis in 7.1%. An NGS epilepsy gene panel (EP) was done for 45 patients, leading to a diagnosis in 24.4% (n = 11). Exome sequencing was done for 27 patients, including n = 14 with non-diagnostic panel testing; it led to a diagnosis in 37.3% (n = 10). Exome sequencing led to a diagnosis in 61.5% of patients without a previous panel test and in only two patients who had previously had a negative panel testing. SIGNIFICANCE: In this article, we present the diagnostic evaluations of ES for a cohort of 123 patients and discuss the yield and priority of NGS for evaluating ES. Our findings suggest that exome sequencing has a higher diagnostic yield for determining the etiology of ES in patients for whom the etiology is still unclear after an appropriate clinical assessment and a brain MRI.
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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 | 0.001 |
| 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".