Comprehensive Examination of Therapies for Pain in Parkinson’s Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Pain in Parkinson's disease (PD) is a debilitating symptom with a prevalence of 68%, yet is untreated 50% of the time. What is unclear, however, is which treatment is optimal for minimizing pain severity in PD. Thus, the objective of this systematic review and meta-analysis was to investigate the efficacy of a variety of novel, complimentary, and conventional treatments for pain in PD and elucidate which therapy is the most effective. A systematic search was performed using MEDLINE, PsycINFO, Embase, CINAHL, and CENTRAL databases. To identify additional articles, manual searches of reference lists of included trials were also searched. Major neurology conference proceedings occurring between January 2014 and February 2018 were also searched to identify unpublished studies that may be potentially eligible. Twenty-five randomized controlled trials that encompassed medical, surgical, and complementary therapies met our inclusion criteria and exhibited moderate quality evidence. Two reviewers conducted assessments for study eligibility, risk of bias, data extraction, and quality of evidence rating. A conservative random-effects model was used to pool effect estimates of pain severity. The greatest reductions in pain were found with safinamide (Standardized mean difference = -4.83, 95% CI [-5.07 to -4.59], p < 0.0001), followed by cannabinoids and opioids, multidisciplinary team care, catechol-O-methyltransferase inhibitors, and electrical and Chinese therapies. Moderate effects in reducing pain were in pardoprunox and surgery, while the weakest effects were in dopaminergic agonists and miscellaneous therapies. Safinamide is an important adjunct to standard parkinsonian medication for alleviating pain in PD.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.053 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".