Interictal high frequency oscillations correlating with seizure outcome in patients with widespread epileptic networks in tuberous sclerosis complex
Bibliographic record
Abstract
OBJECTIVE: Multiple tubers in patients with tuberous sclerosis complex (TSC) often are responsible for drug-resistant epilepsy. The complexity of the epileptic network formed by multiple tubers complicates localization of the epileptogenic zone that is needed to design a surgical treatment strategy. High frequency oscillations (HFOs) on intracranial video-electroencephalography (IVEEG) may be a valuable surrogate marker for the localization of the epileptogenic zone. The purpose of this study was to test the hypothesis that high occurrence rate (OR) of interictal HFOs can guide the localization of the epileptogenic zone. METHODS: We analyzed the OR of interictal HFOs at 80-200 Hz (ripples) and >200 Hz (fast ripples, FRs). We divided OR of interictal HFOs between high and low rates by thresholding. We analyzed the correlation between seizure outcomes using Engel classification and the resection ratio of the seizure onset zone (SOZ), and high-OR HFOs using ordinal logistic regression analysis. RESULTS: We collected 10 patients. The seizure outcomes resulted in Engel classification I in three patients, II in four, III in one, and IV in two. High-OR ripples (5-57 [mean 29] channels, 1-4 [2.8] lobes) and high-OR FRs (9-66 [mean 27] channels, 1-4 [2.6] lobes) were widely distributed. The resection ratio of SOZ did not show statistically significant correlation with the seizure outcome. The resection ratio of high-OR ripples showed statistically significant correlation with the seizure outcome (p = 0.038). The resection ratio of high-OR FRs showed statistically significant correlation with the seizure outcome (p = 0.048). SIGNIFICANCE: The multiple extensive zones with high-OR HFOs suggest a complex and widespread epileptic network in patients with TSC. In a subset of TSC patients with drug-resistant epilepsy, resection of cortex with both interictal high-OR FRs and ripples on IVEEG correlated with a good seizure outcome.
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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 | 0.002 |
| 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".