Will adaptive deep brain stimulation for Parkinson’s disease become a real option soon? A Delphi consensus study
Bibliographic record
Abstract
While conventional deep brain stimulation (cDBS) treatment delivers continuous electrical stimuli, new adaptive DBS (aDBS) technology provides dynamic symptom-related stimulation. Research data are promising, and devices are already available, but are we ready for it? We asked leading DBS experts worldwide (n = 21) to discuss a research agenda for aDBS research in the near future to allow full adoption. A 5-point Likert scale questionnaire, along with a Delphi method, was employed. In the next 10 years, aDBS will be clinical routine, but research is needed to define which patients would benefit more from the treatment; second, implantation and programming procedures should be simplified to allow actual generalized adoption; third, new adaptive algorithms, and the integration of aDBS paradigm with new technologies, will improve control of more complex symptoms. Since the next years will be crucial for aDBS implementation, the research should focus on improving precision and making programming procedures more accessible.
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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 | 0.200 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".