SPATIO-TEMPORAL CHANGES OF ICTAL HIGH FREQUENCY OSCILLATIONS IN PEDIATRIC NEOCORTICAL EPILEPSY
Bibliographic record
Abstract
Objectives: High-frequency oscillations (HFOs) have been recognized in epileptic regions. We retrospectively analyzed ictal subdural EEG by using multiple band frequency analysis (MBFA) in children with intractable neocortical epilepsy. Our objectives were 1) to define the correlation between the area with HFOs and resection area, 2) to evaluate the distribution of HFOs and the post-surgical seizure outcome. Methods: We retrospectively studied 9 children (4 girls and 5 boys, mean age, 12.2 years; range, 4 to 17 years) who presented with intractable extra-hippocampal localization-related epilepsy and who underwent extraoperative video subdural EEG and focal cortical resections. We performed MBFA to identify the frequency and distribution of HFOs. We compared the frequency and distribution of HFOs before and after the clinical seizure onset and post-surgical seizure outcome. Results: In 4 patients seizure free post-operatively, MBFA showed more electrodes with HFOs inside the resection area than outside in >60% of seizures, both before and after clinical seizure onset. In 5 patients with residual seizures, more electrodes with HFOs were detected outside the resection area than inside in >65% of seizures. Before clinical onset, in all 9 patients, the electrodes with faster HFOs were resected in >2/3 of seizures. After clinical onset, in 4 patients seizure free post-operatively, electrodes with faster HFOs were predominantly resected in 87% of seizures. In 3 of 5 patients with residual seizures, >75% of seizures had electrodes with faster HFOs outside the resection area. Two patients with residual seizures showed faster HFOs inside than outside the resection area after the clinical onset, but there were more electrodes with HFOs outside than inside before and after the clinical onset. Conclusion: Complete resection of the electrodes with HFOs, including the electrodes with fastest HFOs, may achieve the best post-surgical 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".