Idiopathic Parkinson’s disease and chronic pain in the era of deep brain stimulation: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Pain is the most common nonmotor symptom of Parkinson's disease (PD) and is often undertreated. Deep brain stimulation (DBS) effectively mitigates the motor symptoms of this multisystem neurodegenerative disease; however, its therapeutic effect on nonmotor symptoms, especially pain, remains inconclusive. While there is a critical need to help this large PD patient population, guidelines for managing this significant disease burden are absent. Herein, the authors systematically reviewed the literature and conducted a meta-analysis to study the influence of traditional (subthalamic nucleus [STN] and globus pallidus internus [GPi]) DBS on chronic pain in patients with PD. METHODS: The authors performed a systematic review of the literature and a meta-analysis following PRISMA guidelines. Risk of bias was assessed using the levels of evidence established by the Oxford Centre for Evidence-Based Medicine. Inclusion criteria were articles written in English, published in a peer-reviewed scholarly journal, and about studies conducting an intervention for PD-related pain in no fewer than 5 subjects. RESULTS: Twenty-six studies were identified and included in this meta-analysis. Significant interstudy heterogeneity was detected (Cochran's Q test p < 0.05), supporting the use of the random-effects model. The random-effects model estimated the effect size of DBS for the treatment of idiopathic pain as 1.31 (95% CI 0.84-1.79). The DBS-on intervention improved pain scores by 40% as compared to the control state (preoperative baseline or DBS off). CONCLUSIONS: The results indicated that traditional STN and GPi DBS can have a favorable impact on pain control and improve pain scores by 40% from baseline in PD patients experiencing chronic pain. Further trials are needed to identify the subtype of PD patients whose pain benefits from DBS and to identify the mechanisms by which DBS improves pain in PD patients.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".