The effect of repetitive transcranial magnetic stimulation on cognitive impairment in Parkinson’s disease with dementia: Pilot study
Bibliographic record
Abstract
BACKGROUND: The exact mechanism of cognitive impairment in PD is not known. Repetitive transcranial magnetic stimulation (rTMS) has been proposed as a possible treatment for cognitive impairment and to treat the motor symptoms in Parkinson's disease (PD) where its effects seem additive to those of dopaminergic medications. OBJECTIVE: In this pilot study we investigated whether repeated sessions of rTMS have an effect on measures of cognitive impairment in patients with PD dementia. METHODS: 33 patients with PD dementia were randomly assigned sham or real rTMS (2000 pulses; 20 Hz; 90% RMT; 10 trains of 10 s with 25 s between each train) over the hand area of each motor cortex (5 min between hemispheres) for 10 days (5 days/week) followed by 5 booster sessions every month for 3 months. Assessments included the Unified Parkinson's Disease Rating Scale part III (UPDRS), Montreal Cognitive Assessment (MoCA); Mini Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR); Memory and Executive Screening (MES) and Instrumental activity of Daily Living (IADL). Event related potentials (P300) and cortical excitability were measured before treatment and after the last session. RESULTS: There were no significant differences in the effects of rTMS between groups. Although rTMS improved motor function in the active group it had only a minor effect on two of the dementia rating scores (the MMSE and MoCA) but not the others (CDR and MES). There was also a reduction in the latency of the P300 in the active group. CONCLUSIONS: rTMS over M1 is useful for motor function and may have a small positive effect on cognition. However, better approaches for the latter are necessary, may be require multisite rTMS to target both motor and frontal cortical region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".