Clinical trials for deep brain stimulation: Current state of affairs
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is a surgical neuromodulation procedure with a historically wide range of possible therapeutic indications, including movement disorders, neuropsychiatric conditions, and cognitive disorders. Ongoing research in this field is critical to gain further insights into the mechanisms of DBS, to discover novel brain targets for new and existing indications, and to refine targeting and post-operative programming techniques for the optimization of therapeutic outcomes. OBJECTIVE: To update on the state of DBS-related clinical human research by cataloging and summarizing clinical trials that have been completed or are currently ongoing in this field worldwide. METHODS: A search was conducted for clinical trials pertaining to DBS, currently listed on the ClinicalTrials.gov database. Trials were analyzed to generate a detailed overview of ongoing DBS-related research. Specifically, trials were categorized by trial start date, study completion status, clinical phase, projected subject enrollment, disorder, brain target, country of origin, device manufacturer, funding source, and study topic. RESULTS: In total, 384 relevant clinical trials were identified. The trials spanned 28 different disorders across 26 distinct brain targets, with almost 40% of trials being for conditions other than movement disorders. The majority of DBS trials have been US-based (41.9% of studies) but many countries are becoming increasingly active. The ratio of investigator-sponsored to industry-sponsored trials was 3:1. Emphasizing the need to better understand the mechanism of action of DBS, one-third of the studies predominantly focus on imaging or electrophysiological changes associated with DBS. CONCLUSIONS: This overview of current DBS-related clinical trials provides insight into the status of DBS research and what we can anticipate in the future concerning new brain targets, indications, techniques, and developing a better understanding of the mechanisms of action of DBS.
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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 | 0.041 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".