Cognitive function fifty-six years after surgical treatment of temporal lobe epilepsy: A case study
Bibliographic record
Abstract
We report a long-term follow-up investigation of a patient who was operated in 1954 to relieve intractable temporal lobe seizures characterized by automatism and amnesia. Neuropsychological review at 16 months after surgery showed a slight residual impairment of verbal comprehension and verbal recall and good nonverbal skills. Seizure-free since the operation except for two attacks in the early postoperative years, the patient has been off medication for 25 years and has pursued a successful career as an artist. Our investigation at 56 postoperative years focused on cognitive skills, with some emphasis on learning and memory; a clinical examination was also performed, and the anatomical extent of the resection was determined on 3-Tesla magnetic resonance imaging. Four age- and IQ-appropriate women were tested as healthy control subjects. The patient showed material-specific impairments in language and verbal memory compared with the control subjects and also compared with her own earlier performance, but her performance on other cognitive tasks did not differ from that of the control subjects. Thus, her specific deficits had worsened over time, and she was also impaired compared with healthy individuals of her age, but her deficits remained confined to the verbal sphere, consistent with her temporal lobe seizure focus and surgery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".