Adaptive Deep Brain Stimulation in Parkinson’s Disease: A Delphi Consensus Study
Bibliographic record
Abstract
Importance: If history teaches, as cardiac pacing moved from fixed-rate to on-demand delivery in in 80s of the last century, there are high probabilities that closed-loop and adaptive approaches will become, in the next decade, the natural evolution of conventional Deep Brain Stimulation (cDBS). However, while devices for aDBS are already available for clinical use, few data on their clinical application and technological limitations are available so far. In such scenario, gathering the opinion and expertise of leading investigators worldwide would boost and guide practice and research, thus grounding the clinical development of aDBS. Observations: We identified clinical and academically experienced DBS clinicians (n=21) to discuss the challenges related to aDBS. A 5-point Likert scale questionnaire along with a Delphi method was employed. 42 questions were submitted to the panel, half of them being related to technical aspects while the other half to clinical aspects of aDBS. Experts agreed that aDBS will become clinical practice in 10 years. In the present scenario, although the panel agreed that aDBS applications require skilled clinicians and that algorithms need to be further optimized to manage complex PD symptoms, consensus was reached on aDBS safety and its ability to provide a faster and more stable treatment response than cDBS, also for tremor-dominant Parkinson's disease patients and for those with motor fluctuations and dyskinesias. Conclusions and Relevance: Despite the need of further research, the panel concluded that aDBS is safe, promises to be maximally effective in PD patients with motor fluctuation and dyskinesias and therefore will enter into the clinical practice in the next years, with further research focused on algorithms and markers for complex symptoms.
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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 | 0.158 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".