Orbitofrontal Epilepsy: Case Series and Review of Literature (P6.341)
Bibliographic record
Abstract
OBJECTIVE To better characterize orbitofrontal epilepsy. BACKGROUND Temporal lobe epilepsy is the most common and studied focal refractory epilepsy. Orbitofrontal epilepsy is less known, possibly unrecognized. DESIGN/METHODS A) Retrospective chart analysis of orbitofrontal epilepsy cases investigated in 6 epilepsy monitoring units between 1988-2014; B) Exhaustive review of all case reports of orbitofrontal epilepsy in literature from 1972-2014. Diagnosis was confirmed by the presence of an orbitofrontal epileptogenic lesion or seizure-freedom following orbitofrontal resection. RESULTS Seventeen cases (7M/10F; mean age 33 yo(8-51); mean age of onset 14.5 years) were identified. Semiologically, 4(24[percnt]) experienced auras, 2(11[percnt]) had a fear component, 11(65[percnt]) were dyscognitive, 11(65[percnt]) experienced gestural motor behaviors, 6(35[percnt]) had verbal automatisms. Scalp EEG interictal epileptiform discharges were localized to the frontal, temporal or fronto-temporal leads, lateralized or predominating ipsilaterally. 3/10(30[percnt]) and 1/5(20[percnt]) of patients had a localizing PET or ictal SPECT study, respectively. Out of the 15 patients who eventually underwent surgery, 8 had a lesion on MRI, all had a favourable outcome (66[percnt] Engel 1; 34[percnt] Engel 2; mean FU 5 years). Pathological analysis of 14/15 of the resected specimen revealed: focal cortical dysplasia (9), low grade glioma (1), cavernoma (1), oligodendroglial hyperplasia (1) and normal (2). Our review of the literature identified only 31 cases where orbitofrontal epilepsy could be confidently established. CONCLUSIONS To our knowledge, this is largest orbitofrontal epilepsy series reported. Overall, auras are infrequent and mostly non-specific, generally with altered consciousness, motor gestural behaviors and verbal automatisms. Interictal epileptiform discharges are mainly over the frontal and temporal leads. Ictal discharges are more diffuse. Surgical outcome is generally good but neuropsychological outcome is less well documented. Our findings are limited by the retrospective nature of our study.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".