High‐frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery
Bibliographic record
Abstract
OBJECTIVE: High-frequency oscillations (HFOs) in the intracerebral electroencephalogram (EEG) have been linked to the seizure onset zone (SOZ). We investigated whether HFOs can delineate epileptogenic areas even outside the SOZ by correlating the resection of HFO-generating areas with surgical outcome. METHODS: Twenty patients who underwent a surgical resection for medically intractable epilepsy were studied. All had presurgical intracerebral EEG (500Hz filter and 2,000Hz sampling rate), at least 12-month postsurgical follow-up, and a postsurgical magnetic resonance imaging (MRI). HFOs (ripples, 80-250Hz; fast ripples, >250Hz) were identified visually during 5 to 10 minutes of slow-wave sleep. Rates and extent of HFOs and interictal spikes in resected versus nonresected areas, assessed on postsurgical MRIs, were compared with surgical outcome (Engel's classification). We also evaluated the predictive value of removing the SOZ in terms of surgical outcome. RESULTS: The mean duration of follow-up was 22.7 months. Eight patients had good (Engel classes 1 and 2) and 12 poor (classes 3 and 4) surgical outcomes. Patients with a good outcome had a significantly larger proportion of HFO-generating areas removed than patients with a poor outcome. No such difference was seen for spike-generating regions or the SOZ. INTERPRETATION: The correlation between removal of HFO-generating areas and good surgical outcome indicates that HFOs could be used as a marker of epileptogenicity and may be more accurate than spike-generating areas or the SOZ. In patients in whom the majority of HFO-generating tissue remained, a poor surgical outcome occurred.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".