Seizures in children with dysembryoplastic neuroepithelial tumors of the brain—A review of surgical outcomes across several studies
Bibliographic record
Abstract
PURPOSE: In children and adolescents, dysembryoplastic neuroepithelial tumors (DNETs) of the brain present with seizures almost 100% of the time, potentially creating significant long-term morbidity and disability despite the generally indolent course of the lesion. These tumors also tend to be quite resistant to anti-epileptic drugs which, themselves, can be associated with long-term side effects and resultant disability. Many clinicians advocate early surgical resection of these lesions, but how effective this approach is, and how aggressive tumor removal should be, continues to be debated. METHODS: We performed a systematic review of the relevant literature to identify all reports of DNET resections in pediatric patients published over the past 20 years. In all, over 3000 MEDLINE abstracts were reviewed, ultimately resulting in 13 studies with 185 pediatric DNET patients to review. RESULTS: Surgical resection of the lesion was effective at improving seizures in over 98% of patients and at achieving long-term seizure freedom in 86%. Surgical resection of DNETs also appeared to be quite safe, with no reported perioperative deaths and an overall rate of postoperative complications of 12%; the vast majority of these complications were transient. CONCLUSIONS: Total gross resection of the lesion was the only factor statistically correlated with long-term seizure freedom (r = 0.63, p = 0.03). However, data remain lacking regarding whether this translates into more extensive procedures-like brain mapping and partial lobectomies-being any more effective than simple lesionectomies alone. Further research is clearly needed to address this and other crucial questions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".