Language tasks used for the presurgical assessment of epileptic patients with MEG
Bibliographic record
Abstract
Determining the language dominant hemisphere and the intrahemispheric localization of this function are imperative in the planning of neurosurgical procedures in epileptic patients. New noninvasive diagnostic techniques are being developed to reduce the risks associated with more invasive techniques. The aim of this paper is to review the different protocols for lateralizing and/or localizing language functions using magnetoencephalography (MEG), a noninvasive technique. The reviewed studies include control and patient populations using various protocols which employ different expressive and receptive language tasks. The overall findings reveal high concordance between MEG and the intracarotid amobarbital test (IAT). Moreover, MEG allows intrahemispheric localization of receptive and expressive language functions. However, the different language tasks used with MEG, whether receptive or expressive, appear to activate the left temporal more than frontal areas. The best task to assess language comprehension in both adults and children appears to be a word recognition task. A verbal fluency task could be used to test language production in children and a verb generation task in adults.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".