Intraoperative Electrocorticography in Temporal Lobe Epilepsy Surgery
Bibliographic record
Abstract
Although in clinical use for many years, the validity of intraoperative electrocorticography (ECoG) in guiding resective temporal lobe epilepsy (TLE) surgery is uncertain. Advances in neuroimaging and extraoperative intracranial recordings have contributed greatly to the identification of epileptogenic lesions and cortex, clarifying the limitations of a brief intraoperative interictal recording. Studies of undifferentiated ECoG findings (which classify all interictal cortical spike discharges as equal) tend to not support this method. This article reviews ECoG and presents data from 86 TLE surgeries at the University of British Columbia suggesting that differentiation of ECoG features may enhance the contribution of this time honored method. Specifically, independent foci may be more important for epileptogenesis than synchronous foci, and postexcision activation appears to be a benign phenomenon, while residual spikes unaltered by the resection correlate with a greater proportion of seizure recurrence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".