Current Status of Pain Management in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Pain is a non-motor symptom in Parkinson's disease (PD) which commonly goes underreported. Adequate treatment for pain in PD remains challenging, and to date, no clear guidelines for management are available. METHODS: With the goal of understanding and organizing the current status of pain management in PD, we conducted a review of pharmacological and non-pharmacological treatments for pain in patients with PD. Suitable studies cataloged in PubMed and the Cochrane database up to October 31, 2019, were included prioritizing randomized controlled trials. Post-hoc analyses and open-label studies were also included. RESULTS: Treatment with levodopa increases pain thresholds in patients with PD. Apomorphine did not have similar efficacy. Duloxetine provided benefit in an open-label trial. Oxycodone-naloxone PR did not have a significant improvement in pain, but per-protocol analysis showed a reduction in pain when adherence was strong. Rotigotine patch had numerical improvement on pain scales with no statistical significance. Safinamide significantly improved the "bodily discomfort" domain in the PDQ-39 questionnaire. Botulinum toxin A had a non-significant signal toward improving dystonic limb pain in PD. DBS to the subthalamic nucleus may modulate central pain thresholds, and a pilot study of cranioelectric therapy warrants future research in the area. CONCLUSION: After optimizing dopaminergic therapy, understanding the type of pain a patient is experiencing is essential to optimizing pain control in PD. While recommendations can be made regarding the treatment options in each domain, evidence remains weak and future randomized controlled studies are needed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".