A modified Delphi study of post-operative management for subthalamic deep brain stimulation in Parkinson's disease
Bibliographic record
Abstract
BackgroundDespite the widespread adoption of deep brain stimulation (DBS) for treating Parkinson's disease (PD) over the last few decades, standardized post-operative care protocols remain lacking.ObjectiveThis study aimed to establish expert consensus on managing post-operative subthalamic nucleus (STN-DBS).MethodsA three-round online Delphi study was conducted involving an international panel of DBS experts actively engaged in all facets of post-operative care. In the initial round, the panel generated ideas regarding essential components of a post-operative care protocol. In rounds two and three, numerical ratings and rankings were employed to achieve consensus on the formulated statements. This iterative process culminated in a refined STN-DBS care protocol.ResultsThe study included 76 international participants who, over three survey rounds, reached consensus on 129 components of a care protocol for managing post-operative STN-DBS. The final protocol encompassed eleven essential domains: hospital discharge, rehabilitation referral, imaging and lead review, monopolar testing, local field potential sensing, troubleshooting, medication management, multiprofessional care, follow-up, empowerment of patients and caregivers, and quality control of management procedures.ConclusionsThis Delphi-based, expert-driven process resulted in a comprehensive care protocol for patients undergoing STN-DBS. The findings offer a valuable resource for healthcare professionals, providing a structured, consensus-based framework aimed at optimizing post-operative outcomes. In addition to supporting clinical practice, these recommendations may help inform policy development and drive systematic improvements in care delivery. Further research and validation in diverse clinical settings will be essential to assess the generalizability and real-world impact of the proposed procedures.
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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 | 0.149 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".