P.098 Usefulness of language mapping during cortical stimulation for presurgical planning in Stereo-encephalograph
Bibliographic record
Abstract
Background: Background: Language determination is a pivotal part of presurgical investigations. Presurgical cortical stimulation (CS) with language mapping (LM) in patients with intracranial recordings (SEEG) is a growing practice in some Comprehensive Epilepsy Centers. Methods: Methods: This retrospective, single center study included patients implanted with SEEG that underwent CS for LM in our Epilepsy Monitoring Unit. We describe frequencies, demographic characteristics of these patients and whether or not CS with LM was useful. Results: Results: From January 2015 to June2021, a total of 177 patients were implanted with SEEG and analyzed. 95 patients had CS and 44 of these had CS with LM. The mean age was 33 (ranging from 15-70). During LM, anomia was induced in 26 (58%), speech arrest in 22 (49%), paraphasic errors in 13 (29%), and hesitation in 9 (20%). LM results were recorded as influencing surgical decision in 7 (16%) patients, 4 (9%) did not undergo surgery due to expected language deficits and 3 (7%) proceeded with surgery due to an acceptable risk of language deficit. Conclusions: Conclusions: Cortical stimulation language mapping is useful for decision-making in presurgical evaluation and should be encouraged whenever involvement of language is suspected when determining the epileptogenic zone.
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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 | 0.000 |
| 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) | 0.006 | 0.001 |
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".