From lateral to mesial: The quest for a surgical cure for temporal lobe epilepsy
Bibliographic record
Abstract
PURPOSE: A gap of more than a hundred years occurred between the first accounts of mesial temporal sclerosis and recognition of its role in the pathogenesis of psychomotor seizures. This paper reviews how the understanding and surgical treatment of temporal lobe epilepsy developed, particularly from the work of Penfield, Jasper, and their associates at the Montreal Neurological Institute (MNI). METHODS: Publications on EEG and surgery for temporal lobe seizures from 1935 to 1953 were reviewed and charts of selected patients operated on at the MNI in the same period were examined. Attention was focused on the evolution of surgical techniques for temporal lobe epilepsy. RESULTS: In the late 1930s, some EEG findings suggested deep-lying disturbances originating in the temporal lobe. However, it took another two decades before the correlation of clinical, neurophysiological, and anatomical findings provided evidence for the involvement of the mesial structures in psychomotor or temporal lobe seizures. From 1949 and onward, Penfield and his associates applied this evidence to extend the surgical resections to include the uncus and the hippocampus. CONCLUSION: The collaborative work of a team led by Penfield and Jasper at the MNI helped to define the role of neurophysiological studies in epilepsy surgery. As a result, the importance of removing the mesial structures in order to obtain better seizure control in patients with temporal lobe epilepsy became firmly established.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".