Method of invasive monitoring in epilepsy surgery and seizure freedom and morbidity: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Invasive monitoring is sometimes necessary to guide resective surgery in epilepsy patients, but the ideal method is unknown. In this systematic review, we assess the association of postresection seizure freedom and adverse events in stereoelectroencephalography (SEEG) and subdural electrodes (SDE). METHODS: We searched three electronic databases (MEDLINE, Embase, and CENTRAL [Cochrane Central Register of Controlled Trials]) from their inception to January 2018 with the keywords "electroencephalography," "intracranial grid," and "epilepsy." Studies that presented primary quantitative patient data for postresection seizure freedom with at least 1 year of follow-up or complication rates of SEEG- or SDE-monitored patients were included. Two trained investigators independently collected data from eligible studies. Weighted mean differences (WMDs) with 95% confidence interval (CIs) were used as a measure of the association of SEEG or SDE with seizure freedom and with adverse event outcomes. RESULTS: Of 11 462 screened records, 48 studies met inclusion criteria. These studies reported on 1973 SEEG patients and 2036 SDE patients. Our systematic review revealed SEEG was associated with 61.0% and SDE was associated with 56.4% seizure freedom after resection (WMD = +5.8%, 95% CI = 4.7-6.9%, P = .001). Furthermore, SEEG was associated with 4.8% and SDE was associated with 15.5% morbidity (WMD = -10.6%, 95% CI = -11.6 to -9.6%, P = .001). SEEG was associated with 0.2% mortality and SDE was associated with 0.4% mortality (WMD = -0.2%, 95% CI = -0.3 to -0.1%, P = .001). SIGNIFICANCE: In this systematic review of SEEG and SDE invasive monitoring techniques, SEEG was associated with fewer surgical resections yet better seizure freedom outcomes in those undergoing resections. SEEG was also associated with lower mortality and morbidity than SDE. Clinical studies directly comparing these modalities are necessary to understand the relative rates of seizure freedom, morbidity, and mortality associated with these techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".