The First Case of Invasive EEG Monitoring for the Surgical Treatment of Epilepsy: Historical Significance and Context
Bibliographic record
Abstract
PURPOSE: Controversy persists about when EEG became a fundamental tool in the preoperative investigation for epilepsy surgery. We revisit Penfield's first use of invasive EEG monitoring, emphasizing its historical importance for the evolution of epilepsy surgery. METHODS: Patients' hospital charts and articles published before 1940 regarding EEG and epilepsy or EEG and cerebral lesions were reviewed to evaluate the historical context of the surgery. RESULTS: In April 1939, Penfield performed trephination over both temporal regions and placed electrodes on the dura, intending to lateralize seizure origin in a patient with bitemporal epilepsy. The patient underwent serial EEGs with this technique. The final report of the recordings from epidural leads was "continued random delta activity in the left temporal region indicating a cortical lesion on this side." The pneumoencephalogram showed "the presence of diffuse cerebral atrophy, particularly in the left cerebral hemisphere." Based on these findings, the patient underwent surgery on April 21, revealing a meningocerebral scar in the posterior part of the left temporal lobe. Brain stimulation and electrocorticography delineated the extent of resection, while preserving the speech area. Seizures did not improve. CONCLUSIONS: We revisit the first case of epidural EEG monitoring for epilepsy surgery and show that the concept of EEG-directed surgery was already present at the Montreal Neurological Institute in the late 1930s.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".