Clinical outcomes using ClearPoint interventional MRI for deep brain stimulation lead placement in Parkinson’s disease
Bibliographic record
Abstract
OBJECTIVE: The ClearPoint real-time interventional MRI-guided methodology for deep brain stimulation (DBS) lead placement may offer advantages to frame-based approaches and allow accurate implantation under general anesthesia. In this study, the authors assessed the safety and efficacy of DBS in Parkinson's disease (PD) using this surgical method. METHODS: This was a prospective single-center study of bilateral DBS therapy in patients with advanced PD and motor fluctuations. Symptom severity was evaluated at baseline and 12 months postimplantation using the change in Unified Parkinson's Disease Rating Scale (UPDRS) Part III "off" medication score as the primary outcome variable. RESULTS: Twenty-six PD patients (15 men and 11 women) were enrolled from 2010 to 2013. Twenty patients were followed for 12 months (16 with a subthalamic nucleus target and 4 with an internal globus pallidus target). The mean UPDRS Part III "off" medication score improved from 40.75 ± 10.9 to 24.35 ± 8.8 (p = 0.001). "On" medication time without troublesome dyskinesia increased 5.2 ± 2.6 hours per day (p = 0.0002). UPDRS Parts II and IV, total UPDRS score, and dyskinesia rating scale "on" medication scores also significantly improved (p < 0.01). The mean levodopa equivalent daily dose decreased from 1072.5 ± 392 mg to 828.25 ± 492 mg (p = 0.046). No significant cognitive or mood declines were observed. A single brain penetration was used for placement of all leads, and the mean targeting error was 0.6 ± 0.3 mm. There were 3 serious adverse events (1 DBS hardware-related infection, 1 lead fracture, and 1 unrelated death). CONCLUSIONS: DBS leads placed using the ClearPoint interventional real-time MRI-guided method resulted in highly accurate lead placement and outcomes comparable to those seen with frame-based approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".