Real-world outcomes of Deep Brain Stimulation for dystonia treatment: Protocol for a prospective, multicenter, international registry
Bibliographic record
Abstract
INTRODUCTION: Deep Brain Stimulation (DBS) is an established therapeutic approach for the treatment of dystonia. However, to date, no large-scale or comprehensive DBS dystonia patient registry has been yet undertaken. Here, we describe the protocol for a world-wide registry of clinical outcomes in dystonia patients implanted with DBS. METHODS AND ANALYSIS: This protocol describes a multicenter, international clinical outcomes registry consisting of up to 200 prospectively enrolled participants at up to 40 different sites to be implanted with a constant-current, multiple independent current controlled (MICC) DBS device (Vercise DBS Systems, Boston Scientific) for treatment of dystonia. Key inclusion criteria for registry candidates include the following: understanding of study requirements and treatment procedures, a signed written informed consent form prior to participation, and meeting all criteria established in the locally applicable Instructions for Use (IFU) for the implanted DBS system. Key clinical endpoints include (but are not limited to) the evaluation of disease state (Burke-Fahn-Marsden Dystonia Rating Scale [BFMDRS], Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), quality of life (Short Form Health Survey-36, Short Form Health Survey-10), and treatment satisfaction (Clinical Global Impression of Change [CGI-Clinician; CGI-Subject; CGI-Caregiver]) at 6-months, 12-months, 2-years, and 3-years post-lead placement. Adverse events are documented and reported using structured questionnaires. PERSPECTIVES: Treatment of patients with dystonia using DBS has progressed considering recent technological advances. This international dystonia outcomes registry aims to collect and evaluate real-world clinical data derived from patients who have been implanted with a constant-current, MICC-equipped DBS system (with available directional capabilities), per standard of care.
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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 | 0.087 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".