P.090 Investigating deep brain stimulation parameters for drug resistant epilepsy treatment: a literature review
Bibliographic record
Abstract
Background: Drug-resistant epilepsy (DRE), defined by persistent seizures despite appropriate anti-seizure medication trials, affects about one-third of individuals with epilepsy. Deep brain stimulation (DBS) has emerged as a promising avenue for improved seizure control. This project reviews existing publications to better understand the neuromodulation parameters used in DBS, aiming to inform clinical decisions on optimizing treatment parameters in patients living with DRE. Methods: A comprehensive literature search of PubMed and Google Scholar was conducted using the keywords “DBS,” “epilepsy,” and “parameters.” Only original studies reporting specific stimulation parameters were included, with meta-analyses and review papers excluded. A weighted Pearson correlation, using study sample size as the weight, examined frequency, pulse width, seizure reduction, and responder rate. Results: So far, 28 studies (1997-2024) have been reviewed, encompassing a total of 1,054 patients, with study size ranging from 1-250 patients. Electrode targets included the hippocampus, ANT, amygdala, centromedian nucleus, and STN. DBS frequencies ranged from 60–333 Hz, and pulse widths from 40–450 µs. Pearson correlation results suggest moderate frequencies (130–145 Hz) and wider pulse widths (300–450 µs) correlate with better seizure reduction and higher responder rates. Conclusions: These results support a formal meta-analysis to further investigate neuromodulation parameters to improve outcomes for DRE patients.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".