Lesionectomy of MRI Detected Lesions in Children with Epilepsy
Bibliographic record
Abstract
The results of complete excision of cerebral lesions detected by MRI in 18 children presenting with epilepsy were analyzed. There were 14 boys and 4 girls with a mean age of 9.2 years. The average age of onset of seizures was 6.8 years. The mean time from onset of seizures to surgery was 2.3 years. Often, CT scans suggested that the lesions were indolent. MRI was better in differentiating neoplastic from developmental lesions. Angiography was non-contributory in this series. Interictal EEGs showed epileptiform activity correlating with imaging studies in 54% of children. The lesion was completely surgically excised in all patients. This was confirmed by intra-operative ultrasound and postoperative imaging. Electrocorticography was performed prior to and after the resection, but residual spiking did not lead to further resection. The average postoperative follow-up was 5.7 years. Five patients had low grade astrocytomas, 4 had gangliogliomas, 1 a mixed astrocytoma-oligodendroglioma, 3 had cortical dysplasia, 2 infantile desmoplastic gangliogliomas, 2 hamartomata, and 1 cavernous angioma. Sixteen patients have been seizure-free since surgery. Only 2 have partial seizures. Thus, all patients benefited from the resection, with respect to seizure control. In those with temporal lobe lesions, improvement in IQ was seen postoperatively. Early consideration of surgery in patients with epilepsy and lesions demonstrated by MRI is suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".