Long-term outcomes after surgery for catastrophic epilepsy in infants: institutional experience and review of the literature
Bibliographic record
Abstract
OBJECTIVE: Uncontrolled epilepsy is associated with serious deleterious effects on the neurological development of infants and has been described as "catastrophic epilepsy." Recently, there has been increased emphasis on early surgical interventions to preserve or rescue neurodevelopmental outcomes in infants with early intractable epilepsy. The enthusiasm for early treatments is often tempered by concerns regarding the morbidity of neurosurgical procedures in very young patients. Here, the authors report outcomes following the surgical management of infants (younger than 1 year). METHODS: The authors performed a retrospective review of patients younger than 1 year of age who underwent surgery for epilepsy at Miami (Nicklaus) Children's Hospital and Jackson Memorial Hospital between 1994 and 2018. Patient demographics, including the type of interventions, were recorded. Seizure outcomes (at last follow-up and at 1 year postoperatively) as well as complications are reported. RESULTS: Thirty-eight infants (median age 5.9 months) underwent a spectrum of surgical interventions, including hemispherectomy (n = 17), focal resection (n = 13), and multilobe resections (n = 8), with a mean follow-up duration of 9.1 years. Hemimegalencephaly and cortical dysplasia were the most commonly encountered pathologies. Surgery for catastrophic epilepsy resulted in complete resolution of seizures in 68% (n = 26) of patients, and 76% (n = 29) had a greater than 90% reduction in seizure frequency. Overall mortality and morbidity were 0% and 10%, respectively. The latter included infections (n = 2), infarct (n = 1), and immediate reoperation for seizures (n = 1). CONCLUSIONS: Surgical intervention for catastrophic epilepsy in infants remains safe, efficacious, and durable. The authors' work provides the longest follow-up of such a series on infants to date and compares favorably with previously published series.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".