Factors underlying scalp‐EEG interictal epileptiform discharges in intractable frontal lobe epilepsy
Bibliographic record
Abstract
AIMS: Scalp-EEG interictal epileptiform discharges (IEDs) may be less predictive of the outcome of frontal lobe epilepsy surgery than of temporal lobe epilepsy surgery. We identified factors associated with the location of scalp-EEG IEDs in intractable frontal lobe epilepsy. METHODS: Ten factors were assessed in a retrospective review of 53 patients with either concordant (frontal lobe seizure focus) or discordant (generalized or outside frontal seizure focus) IED or both, who had excellent surgical outcomes. The Fisher exact test and the Wilcoxon rank sum test determined statistically significant associations. RESULTS: Thirty-six patients (68%) had concordant IED, 24 (45%) discordant IED, and 17 (32%) both. Younger age at onset was significantly associated with discordant IED (mean, 7.5 years versus 17 years for patients without discordant IED; P < 0.01), whereas duration of epilepsy was not. Seizure foci at the frontal convexity were associated with concordant IED. About 72% of patients with a convexity seizure focus had concordant IED, compared with only 33% of patients with mesial frontal foci having concordant IED (P = 0.06). CONCLUSIONS: Early seizure onset in intractable frontal lobe epilepsy is associated with IEDs discordant with seizure focus. Frontal convexity seizure foci are more likely than mesial frontal seizure foci to be associated with concordant discharges.
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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 | 0.003 |
| 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) | 0.001 | 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".