Pathology and Neuroimaging in Pediatric Temporal Lobectomy for Intractable Epilepsy
Bibliographic record
Abstract
OBJECTIVES: Firstly, to study the pathology at surgery in children undergoing temporal lobectomy for intractable partial epilepsy. Secondly, to compare neuroimaging techniques (CT, MRI) in the preoperative detection of pathology. Lastly, to examine the surgical outcome in children. METHODS: Forty-two pediatric patients undergoing temporal lobectomy for intractable epilepsy at the Comprehensive Epilepsy Program at the University of Alberta Hospital between the years 1988-1998 were studied. Patients had extensive preoperative investigations including CT and MRI. The pathology at surgery was reviewed and compared to preoperative neuroimaging. Charts were reviewed to determine surgical outcome. RESULTS: Brain tumors were the most common pathology, found in 13/42 patients. Mesial temporal sclerosis (MTS) was found in 8 patients and dual pathology in an additional 5. Focal cortical dysplasia (FCD) was seen in 4 patients, 1 patient had a porencephalic cyst and 4 patients had tubers of tuberous sclerosis. Seven patients had no specific pathology detected. MRI was clearly more sensitive than CT in the detection of pathology. MRI was abnormal in 27/42 cases (64%), while CT scan was found to be abnormal in only 12/39 (31%). Surgical outcome was excellent, with 34/42 patients (80%) having an Engel class I outcome. One patient had significant improvement with an Engel class II outcome, 3 (7%) had little improvement (Engel class III) and 4 (10%) were unchanged (Engel class IV). Three patients (7%) had surgical complications. CONCLUSIONS: A wide variety of developmental pathology is seen following temporal lobectomy for intractable epilepsy of childhood. Brain tumors, FCD and MTS are common. MRI is superior to CT in the detection of pathology, which may be subtle in children. Surgical outcome is excellent, with most children being seizure free and few complications being seen.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".