Postictal Aphasia and Paresis: A Clinical and Intracerebral EEG Study
Bibliographic record
Abstract
BACKGROUND: We examined the lateralizing value of postictal language and motor deficits and studied their underlying mechanisms. PATIENTS AND METHODS: The total sample consisted of 35 patients (26 temporals, 8 frontals, 1 parietal) with a good postsurgical outcome (Engel's class I and II). Postictal examination was blindly reviewed on videotapes. In 15 cases (29 seizures), postictal language manifestations were analyzed in relation with the diffusion of the epileptic discharge recorded by intracerebral EEG. Language dominance was determined by the intracarotid amobarbital test. RESULTS: Postictal aphasia was observed only when (1) seizure originated in the dominant hemisphere and (2) ictal activity spread to language areas (Wernicke and/or Broca areas). When the epileptic focus was in the nondominant hemisphere, no postictal aphasia was observed even if there was secondary generalization of ictal activity affecting the language areas of the dominant hemisphere. Postictal motor deficits also had a strong lateralizing value even when seizures were secondarily generalized. CONCLUSION: Postictal aphasia in temporal epilepsies and postical motor deficits in temporal and extra temporal epilepsies provided excellent lateralizing information. Postictal deficits appear to be the result of inhibitory mechanisms induced by previous ictal activity of the structures related to these functions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".