High‐frequency oscillations, extent of surgical resection, and surgical outcome in drug‐resistant focal epilepsy
Bibliographic record
Abstract
PURPOSE: Removal of areas generating high-frequency oscillations (HFOs) recorded from the intracerebral electroencephalography (iEEG) of patients with medically intractable epilepsy has been found to be correlated with improved surgical outcome. However, whether differences exist according to the type of epilepsy is largely unknown. We performed a comparative assessment of the impact of removing HFO-generating tissue on surgical outcome between temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE). We also assessed the relationship between the extent of surgical resection and surgical outcome. METHODS: We studied 30 patients with drug-resistant focal epilepsy, 21 with TLE and 9 with ETLE. Two thirds of the patients were included in a previous report and for these, clinical and imaging data were updated and follow-up was extended. All patients underwent iEEG investigations (500 Hz high-pass filter and 2,000 Hz sampling rate), surgical resection, and postoperative magnetic resonance imaging (MRI). HFOs (ripples, 80-250 Hz; fast ripples, >250 Hz) were identified visually on a 5-10 min interictal iEEG sample. HFO rates inside versus outside the seizure-onset zone (SOZ), in resected versus nonresected tissue, and their association with surgical outcome (ILAE classification) were assessed in the entire cohort, and in the TLE and ETLE subgroups. We also tested the correlation of resected brain hippocampal and amygdala volumes (as measured on postoperative MRIs) with surgical outcome. KEY FINDINGS: HFO rates were significantly higher inside the SOZ than outside in the entire cohort and TLE subgroup, but not in the ETLE subgroup. In all groups, HFO rates did not differ significantly between resected and nonresected tissue. Surgical outcome was better when higher HFO rates were included in the surgical resection in the entire cohort and TLE subgroup, but not in the ETLE subgroup. Resected brain hippocampal and amygdala volumes were not correlated with surgical outcome. SIGNIFICANCE: In TLE, removal of HFO-generating areas may lead to improved surgical outcome. Less consistent findings emerge from ETLE, but these may be related to sample size limitations of this study. Size of resection, a factor that was ignored and that could have affected results of earlier studies did not influence results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".