A Comprehensive Review of Recent Trends in Surgical Approaches for Epilepsy Management
Bibliographic record
Abstract
Epilepsy is a neurological disorder that affects millions of people worldwide, with a significant proportion of patients experiencing drug-resistant epilepsy, where seizures remain uncontrolled despite medical treatment. This review evaluates the latest surgical techniques for managing epilepsy, focusing on their effectiveness, safety, and the ongoing challenges that hinder their broader adoption. We explored various databases including PubMed, Google Scholar, and Cochrane Library to look for relevant literature using the following keywords: Epilepsy, Resective Surgery, Corpus Collectumy, and Antiepileptic Drugs. A total of 54 relevant articles were found and thoroughly explored. Recent advancements in surgical interventions include resective procedures such as anterior temporal lobectomy, corpus callosotomy, and hemispherectomy, which have been particularly effective in reducing seizures for specific types of epilepsy. Minimally invasive techniques, including laser interstitial thermal therapy and focused ultrasound, are increasingly being used, offering promising outcomes for certain patient groups. Additionally, neuromodulation methods such as deep brain stimulation, vagus nerve stimulation, and responsive neurostimulation provide alternative treatment options, especially for patients who are not suitable candidates for resective surgery. Despite these advancements, the full potential of epilepsy surgery is often underutilized due to various challenges. Inconsistent referral practices, a lack of standardized surgical protocols, and significant socioeconomic barriers continue to limit access to these procedures. Addressing these issues through improved referral processes, better education for healthcare providers and patients, and ensuring equitable access to advanced surgical treatments is crucial for optimizing patient outcomes. Future research should focus on overcoming these barriers and assessing long-term outcomes to further enhance the care of patients with epilepsy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".