Unilateral repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex in Parkinson's Disease: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) may improve motor and non-motor symptoms in Parkinson's disease (PD). This meta-analysis assessed the efficacy of 10 unilateral rTMS sessions in PD. EVIDENCE ACQUISITION: Randomized controlled trials comparing this protocol to sham stimulation in PD were systematically searched in PubMed, EMBASE, and the Cochrane Library through January 2025. Short-term outcomes were assessed immediately after treatment; long-term outcomes were measured 1-6 months later. Mean differences (MDs) with 95% confidence intervals (CIs) were calculated using random-effects models. EVIDENCE SYNTHESIS: Seven trials were included (mean follow-up: 8.7 weeks). Long-term motor function improved significantly (UPDRS part III: MD=-4.97; 95% CI: -6.53 to -3.40; P<0.001), whereas short-term changes were non-significant. Long-term reductions in depressive symptoms were observed on the Hamilton Depression Rating Scale (MD=-2.77; 95% CI: -4.75 to -0.79; P=0.006), the Montgomery-Åsberg Depression Rating Scale (MD=-6.23; 95% CI: -9.78 to -2.68; P<0.001), and the Beck's Depression Inventory (MD=-4.41; 95% CI: -8.34 to -0.48; P=0.028). Sleep quality improved at long-term follow-up (Pittsburgh Sleep Quality Index: MD=-2.51; 95% CI: -5.33 to -0.31; P=0.081). Short-term cognitive gains were observed on the Montreal Cognitive Assessment (MD=1.70; 95% CI: 0.01 to 3.40; P=0.049). CONCLUSIONS: Ten sessions of unilateral rTMS over the DLPFC improved motor and non-motor symptoms.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.023 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".