Surgical and seizure outcome in children with DNETs who underwent epilepsy surgery
Bibliographic record
Abstract
Background: Dysembryoblastic neuroepithelial tumors (DNETs) are benign tumors of the cerebral cortex that most commonly occur in children or young adults. Seizures are a frequent presenting feature, with an incidence of 80-100%, and are often an indication for surgical resection. Methods: We performed a retrospective chart review of children with DNETs who underwent epilepsy surgery between 1998 and 2014. Results: A total of 12 subjects were identified (6 males, 6 females), all of whom had seizures prior to surgical resection. Of these patients, 1 had infantile spasms, 2 had simple partial seizures and 10 had complex partial seizures. Tumors were located in the temporal (n=7), frontal (n=3) or parietal (n=2) cortex. These patients went on to have surgery on average 15 months after seizure onset, 3 had incomplete resections. At an average follow up of 6 years 4 months, all patients were class 1 on Engel’s Classification. All but one subject with rare non-disabling seizures were seizure free, with only 6 on medication. Follow up MR imaging revealed tumor recurrence in 1 subject. Conclusions: Despite differing seizure seminology and tumor location, surgical resection of these low-grade tumors resulted in excellent seizure outcome even in the setting of incomplete tumor resection.
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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 | 0.000 | 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) | 0.001 | 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".