Abnormal Neuroimaging in Patients with Benign Epilepsy with Centrotemporal Spikes
Bibliographic record
Abstract
PURPOSE: Neuroimaging procedures are usually unnecessary in benign epilepsy of childhood with centrotemporal spikes (BECTS) but are often performed before a specific diagnosis has been reached. By definition, BECTS occurs in normal children; however, recent reports have shown that it also can affect children with static brain lesions. We evaluated the prevalence of abnormal neuroimaging in BECTS and assessed whether the lesions had influenced the clinical and EEG expression of this epilepsy. RESULTS: Among 98 consecutive cases first referred between 1984 and 1999, neuroimaging had been performed in 71 (72%) [magnetic resonance imaging (MRI), 20; computed tomography (CT), 59; MRI+CT, eight]. In ten (14.8%), neuroradiologic procedures were abnormal: enlargement of lateral venticles in five cases including a shunted hydrocephalus in two (no etiology in one, neonatal intraventricular hemorrhage in one), a moderate ventricular dilation in one (neonatal distress), a slight ventricular dilation and hypersignal intensities in the white matter in one (premature birth at 27 weeks), and a moderate enlargement of the right temporal horn in one. A right hippocampal atrophy, a biopercular polymicrogyria, a cavum septum pellucidum, a small cystic lesion located in the epiphysis, and an agenesis of the corpus callosum with macrocrania also were observed once each. The outcome was benign in all, in accordance with the overall prognosis of BECTS. CONCLUSIONS: This study confirms that neuroimaging may be abnormal in patients with BECTS and shows that the presence of brain lesions has no influence on the prognosis. Conversely, BECTS can be diagnosed in patients with brain lesions with or without significant neurologic history or abnormalities.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".