P.097 After-discharges and presurgical cortical stimulation in stereo-encephalography in the study of drug-resistant epilepsy
Bibliographic record
Abstract
Background: Background: Cortical stimulation (CS) as a part of presurgical investigations in patients undergoing implantation of depth electrodes (SEEG) is a growing practice in some Comprehensive Epilepsy Centers. After-discharges (AD) are useful to determine epileptogenic tissue within or outside the epileptogenic network. Classification of afterdischarges was proposed by Blume using subdural recordings(1); its utility in SEEG is unknown. Methods: Methods: Single center, retrospective study that included patients with SEEG that underwent CS in the Epilepsy Monitoring Unit. Demographic characteristics were explored and Blume’s proposed AD classification was used to determine whether or not the CS changed surgical outcomes. Results: Results: From January 2015 to June 2021, a total of 177 patients were implanted with SEEG and analyzed. 95 patients had CS and 91 had AD. Morphologies found were: Rhythmic waves in 4 (0.04%), Rhythmic waves evolving into spikes in18 (19%), Polyspike bursts in 14 (15%), Spike-waves in 28 (30%), and sequential spikes in 18 (19%). 12/14 (86%) patients with Spike-waves had Engel I outcome; Engel class IV patients were more likely to have an evolution of morphology and frequency of ADs in 3 patients(75%). Conclusions: Conclusions: The most frequent morphology of ADs seen was spike-waves. AD morphology and duration may predict post-operative seizure outcomes.
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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 | 0.001 | 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) | 0.002 | 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".