Bibliographic record
Abstract
PILEPSY remains one of the most common afflictions in humans, with a prevalence of about 1% in the general population.Since the advent of phenobarbital as a novel therapeutic agent for the prevention of seizures in patients in 1912, the medical and surgical treatments of epilepsy have advanced significantly.There were many early pioneers in epilepsy surgery, including Horsley, Keen, Nancred, and Sachs.1,4 We must remember that their surgical attack on epilepsy was practiced long before we became familiar with the structure and function of the central nervous system, and the fine histoarchitecture of the neuronal networks within the cerebral cortex.Of course, the work of Penfield 2,3 in the 1930s set the stage for the modern revolution of surgical techniques applied to the study of human epilepsy.In this special issue of Neurosurgical Focus, we have provided an update on the state-of-the-art management of epilepsy from both medical and surgical perspectives.We begin with a description of pharmacologically intractable epilepsy and proceed to a review of imaging strategies to identify neurosurgical candidates.It is becoming increasingly more apparent that neuroimaging is playing a pivotal role in our understanding of the causes of epilepsy in children and adults.We then review current techniques for temporal lobectomy, extratemporal cortical resections, corpus callosotomy, and treatment of hypothalamic hamartomas and tuberous sclerosis.The use of deep brain stimulation for epilepsy is discussed as a novel approach to the management of selected patients with intractable epilepsy.Recent adjuncts in the placement of intracranial electrodes, whether by use of neuronavigation or robotics, are described and can now be made widely accessible to most
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".