Non-invasive brain stimulation to enhance sleep quality and architecture in Parkinson's disease: A systematic review and Bayesian network meta-analysis
Bibliographic record
Abstract
Individuals with Parkinson's disease (PD) suffer from sleep disorders and maladaptive alterations in sleep architecture. These disorders, which increase in frequency and severity as the disease progresses, are multifactorial and clinically relevant. Sleep problems drastically reduce quality of life in these patients and are associated with faster cognitive and motor decline. Standard treatments for managing sleep disorders, which mainly include cognitive behavioral therapy and pharmacotherapy, have provided inconsistent results. Non-invasive brain stimulation (NIBS) has been proposed as another potential strategy for improving sleep quality in PD. We conducted a systematic review and Bayesian network meta-analysis (NMA) following the grading of recommendations, assessment, development, and evaluation (GRADE), to determine whether NIBS improves sleep quality and architecture in PD. Evidence from twenty-four studies, including 792 individuals with PD in the early-to-severe disease stages, was summarized. Our NMA indicated that low-frequency repetitive transcranial magnetic stimulation over the dorsolateral prefrontal cortex (rTMS; 0.54, 95 %CrI: 0.13,0.93; moderate certainty) may ameliorate subjective sleep quality compared to a control condition. Our review further suggested that rTMS may enhance objective sleep quality and sleep architecture. Our findings, which should be interpreted cautiously due to a high risk of bias, are examined in the context of sleep disorders 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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".