Infantile epileptic spasm syndrome: predictors of short- and long-term outcomes
Bibliographic record
Abstract
Introduction Infantile epileptic spasm syndrome (IESS) has significant impact on affected children that affects their future seizure control and neurodevelopmental outcomes. The aim of this study is to identify potential short- and long-term predictors of outcomes in children diagnosed IESS. Method This retrospective study evaluated outcomes of seizure control and developmental status in a historical cohort of 60 children with IESS. The predictor variables included: age, treatment regimen, and early treatment response at 14 days, 3 and 6 months on the measured outcomes. Results Among the 60 children in the cohort, 75% had identified etiologies: Genetic (40%), Structural (35%), and unknown causes (25%). Treatment interventions included either vigabatrin monotherapy (58.33%) or hormonal therapy with or without vigabatrin (41.67%). Clinical response at 3 and 6 months significantly correlated with good seizure control ( p = 0.008 and p = 0.007, respectively) and favorable developmental outcome ( p < 0.001) at last follow-up. Logistic regression showed that treatment response at 3 months increased the odds of good seizure control by 7.21 times (95%CI = 1.93–26.91, p = 0.003), after adjusting for age, treatment regimen, and etiology. Genetic and structural etiologies were significantly associated with a higher likelihood of developing epileptic encephalopathy (EE), with odds ratios of 11.79 (95% CI = 2.04–68.06, p = 0.006) for genetic etiology and 10.21 (95% CI = 1.75–59.65, p = 0.010) for structural etiology. Discussion Early treatment response at 3 and 6 months strongly predicts favorable seizure and developmental outcomes in IESS, with poor responders at these time points more likely to develop EE. Genetic and structural etiologies significantly influence EE risk, emphasizing the need for early identification, sustained treatment monitoring, and potential targeted interventions for high-risk subgroups.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".