Residual fast ripples in the intraoperative corticogram predict epilepsy surgery outcome
Bibliographic record
Abstract
OBJECTIVE: We studied whether residual high-frequency oscillations (80-500 Hz; ripples, 80-250 Hz), especially fast ripples (FRs, 250-500 Hz), in post-resection intraoperative electrocorticography (ECoG) predicted seizure recurrence in comparison to residual interictal spikes and ictiform spike patterns. METHODS: We studied, retrospectively, ECoG recorded at 2,048 Hz after resection in a cohort of patients with refractory focal epilepsy. We analyzed occurrence and number of residual FRs, ripples, interictal spikes, and ictiform spike patterns within the last minute of each recording and compared these to seizure recurrence. RESULTS: We included 54 patients (median age 15.5 years) with 25 months median follow-up. Twenty-four patients had recurrent seizures. We found residual FRs, ripples, spikes, and ictiform spike patterns in 12, 51, 38, and 9 patients. Nine out of 12 patients with residual FRs had recurrent seizures (p = 0.016, positive predictive value 75%). Other ECoG events did not predict seizure recurrence. Patients with seizures had higher FR rates than seizure-free patients (p = 0.022). FRs near the resection and in distant pathologic areas could have changed the resection in 8 patients without harming functionally eloquent areas. One seizure-free patient had FRs in distant functionally eloquent areas. CONCLUSIONS: Residual FRs in post-resection ECoG are prognostic markers for seizure recurrence, especially if their number is high. Tailoring could rely on FRs, but requires careful assessment of the ECoG, as FRs in functionally eloquent areas might not be pathologic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".