Prevalence of Nonlesional Focal Epilepsy in an Adult Epilepsy Clinic
Bibliographic record
Abstract
PURPOSE: To evaluate the prevalence of nonlesional focal epilepsy in an adult epilepsy clinic and its refractoriness to antiepileptic drug therapy. BACKGROUND: Focal epilepsy is frequently, but not always, associated with structural epileptogenic lesions identifiable on magnetic resonance imaging (MRI). METHODS: We analyzed the data from all patients evaluated at an adult epilepsy clinic from January 2002 to December 2011. Clinical and paraclinical findings were used to diagnose focal epilepsy. Magnetic resonance imaging were reviewed and classified as normal, with an epileptogenic lesion, or with a lesion of unclear epileptogenicity. Epileptogenic lesions were further categorized as tumours, vascular malformations, gliosis (including hippocampal atrophy/sclerosis), and malformations of cortical development. Our study group included patients with no lesions on MRI. Pharmacoresistance of patients with nonlesional focal epilepsy was assessed using the ILAE and Perucca's criterias. RESULTS: Out of 1521 patients evaluated (mean age 44 years; range 14-93 years), 843 had focal epilepsy. Magnetic resonance imaging data, available for 806 (96%) subjects, showed epileptogenic lesions in 65%, no obvious epileptogenic lesions in 31% and lesions of unclear epileptogenicity in 4%. Magnetic resonance imaging-identified lesions included gliosis due to an acquired insult (52% including 17% of hippocampal atrophy or sclerosis), tumours (29%), vascular malformations (16%) and malformations of cortical development (10%). Fifty-two percent of nonlesional focal epileptic patients were drug-refractory. CONCLUSION: In a tertiary epilepsy clinic, close to a third of patients with focal epilepsy were found to be nonlesional, half of which were drug-resistant.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".