Spinal Cord Stimulation for Parkinson’s Disease: A Systematic Review and Meta-Analysis of Pain and Motor Outcomes
Bibliographic record
Abstract
BACKGROUND: Spinal cord stimulation (SCS) has been investigated as a potential therapeutic option for managing refractory symptoms in patients with Parkinson's disease (PD). OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the safety and efficacy of SCS in PD. METHOD: A comprehensive literature search was conducted on PubMed and Web of Science to identify SCS studies reporting Unified Parkinson Disease Rating Scale-III (UPDRS-III) or Visual Analogue Scale (VAS) score changes in PD cohorts with at least 3 patients and a follow-up period of at least 1 month. Treatment effect was measured as the mean change in outcome scores and analyzed using an inverse variance random-effects model. The risk of bias was assessed using the Newcastle-Ottawa Scale and funnel plots. RESULTS: A total of 11 studies comprising 76 patients were included. Nine studies involving 72 patients reported an estimated decrease of 4.43 points (95% confidence interval [CI]: 2.11; 6.75, p < 0.01) in UPDRS-III score, equivalent to a 14% reduction. The axial subscores in 48 patients decreased by 2.35 points (95% CI: 1.26; 3.45, p < 0.01, 20% reduction). The pooled effect size of five studies on back and leg pain VAS scores was calculated as 4.38 (95% CI: 2.67; 6.09, p < 0.001), equivalent to a 59% reduction. CONCLUSIONS: Our analysis suggests that SCS may provide significant motor and pain benefits for patients with PD, although the results should be interpreted with caution due to several potential limitations including study heterogeneity, open-label designs, small sample sizes, and the possibility of publication bias. Further research using larger sample sizes and placebo-/sham-controlled designs is needed to confirm effectiveness.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".