Effects of repetitive transcranial magnetic stimulation on gait disorders and cognitive dysfunction in Parkinson's disease: A systematic review with meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is acknowledged to be crucial to manage freezing of gait (FOG) and cognitive impairment for patients with Parkinson's disease (PD), but its effectiveness is unclear. OBJECTIVE: To determine the effects of rTMS on FOG and cognitive function in people with PD and to investigate potential factors that modulate the rTMS effects. METHODS: Databases searched included PubMed, Web of Science, EMBASE, and the Cochrane Library from inception to December 31, 2021. Eligible studies include a controlled randomized clinical trial of rTMS intervention for FOG and cognitive dysfunction in PD patients. The weighted mean difference (WMD) with 95% confidence intervals (CI) were calculated with fixed-effects models. The outcome of the study included gait and cognitive assessments. RESULTS: Sixteen studies with a total of 419 patients were included. Fixed-effects analysis revealed that rTMS was effective in improving freezing of gait questionnaire scores (short-term effect: WMD = -0.925, 95% CI: -1.642 to -0.209, p = .011; long-term effect: WMD = -2.120, 95% CI: -2.751 to -1.489, p = .000), 10-m walking time (short-term effect: WMD = -0.456, 95% CI: -0.793 to -0.119, p = .008; long-term effect: WMD = -0.526, 95% CI: -0.885 to -0.167, p = .004), Timed Up-and-Go scores (short-term effect: WMD = -1.064, 95% CI: -1.555 to -0.572, p = .000; long-term effect: WMD = -1.097, 95% CI: -1.422 to -0.772, p = .000), Montreal cognitive assessment (WMD = 3.714, 95% CI: 2.567 to 4.861, p = .000), and frontal assessment battery (WMD = -0.584, 95% CI: -0.934 to -0.234, p = .001). CONCLUSIONS: RTMS showed a beneficial effect on FOG and cognitive dysfunction in parkinsonism. However, the optimal rTMS protocol has not been determined and further high-quality 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".