Theta Burst Magnetic Stimulation Improves Parkinson’s-Related Cognitive Impairment: A Randomised Controlled Study
Bibliographic record
Abstract
Background. Evidence remains mixed as to the effectiveness of repetitive transcranial magnetic stimulation (rTMS) in treating mild cognitive impairment (MCI) in patients with Parkinson’s disease (PD). Objective. In this study, we examined the short- and long-term effects of patterned rTMS. Methods. We randomly assigned 35 patients with PD with MCI to two groups. One group received intermittent theta burst stimulation (iTBS; n = 20), and the other received its sham counterpart (n = 15). The stimulations were applied over the left dorsolateral prefrontal cortex for 10 consecutive weekdays. Measurements based on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and Montreal Cognitive Assessment (MoCA) were conducted at three time points: at baseline, immediately after the last intervention and at 3-month follow-up. Each patient received a 99mTc-TRODAT-1 single-photon emission computed tomography (SPECT) brain scan at baseline. Results. The iTBS group exhibited significantly greater improvement than the sham group did in total RBANS and MoCA scores ( p < .001 for both) immediately after intervention and at the 3-month follow-up. Radiotracer uptake in the bilateral basal ganglion in baseline SPECT was positively correlated with response to iTBS conditioning with respect to improvements in MoCA scores ( p = .021). Conclusion. This randomised controlled trial provides evidence that a consecutive iTBS protocol can achieve a persistent and wide-ranging therapeutic effect in patients with PD with MCI.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".