Complications of epilepsy surgery—A systematic review of focal surgical resections and invasive <scp>EEG</scp> monitoring
Bibliographic record
Abstract
PURPOSE: Underutilization of epilepsy surgery remains a major problem and is in part due to physicians' misconceptions about the risks associated with epilepsy surgery. The purpose of this study was to systematically review the literature on complications of focal epilepsy surgery. METHODS: A literature search was conducted using PubMed and Embase to identify studies examining epilepsy surgery complications. Abstract and full text review, along with data extraction, was done in duplicate. Minor medical and neurologic complications were defined as those that resolved completely within 3 months of surgery, whereas major complications persisted beyond that time frame. Descriptive statistics were used to report complication proportions. KEY FINDINGS: Invasive monitoring: Minor complications were reported in 7.7% of patients, whereas major complications were reported in only 0.6% of patients undergoing invasive monitoring. Resective surgery: Minor and major medical complications were reported in 5.1% and 1.5% of patients respectively, most common being cerebrospinal fluid (CSF) leak. Minor neurologic complications occurred in 10.9% of patients and were twice as frequent in children (11.2% vs. 5.5%). Minor visual field defects were most common (12.9%). Major neurologic complications were noted in 4.7% of patients, with the most common being major visual field defects (2.1% overall). Perioperative mortality was uncommon after epilepsy surgery, occurring in only 0.4% of temporal lobe patients (1.2%extratemporal). SIGNIFICANCE: The majority of complications after epilepsy surgery are minor or temporary as they tend to resolve completely. Major permanent neurologic complications remain uncommon. Mortality as a result of epilepsy surgery in the modern era is rare.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".