Repetitive Transcranial Magnetic Stimulation in Treatment of Levodopa-Induced Dyskinesia in Parkinson’s Disease
Bibliographic record
Abstract
Background: Dyskinesia is one of the major complications of long-term dopaminergic treatment of Parkinson’s disease (PD), and deep brain stimulation may be the only satisfactory treatment for it. This study aims to search if there is any therapeutic effect of repetitive transcranial magnetic stimulation (rTMS) for levodopa-induced dyskinesia (LID) in PD patients. Methods: This study was conducted in Mansoura International Specialized Hospital in 43 complicated idiopathic PD patients. The patients with LID were divided into two groups matched in age, sex, duration and stage of the disease. Dyskinesia was detected by the Unified PD Rating Scale section IV. The patients of the study group (20 patients) received active rTMS, 5 Hz was applied bilaterally over the motor hand and leg areas of the cortex, in 20 trains, and each train is formed of 100 pulses, with 20-s inter-train interval. Ten sessions were administered once per day for 10 successive days for each patient. The patients of the control group (20 patients) received sham rTMS. Results: After rTMS, there was significant improvement in LID in the study group (P < 0.001), while there was no improvement in the control one (P = 0.585). As regards to the LID clinical presentations, there was no significant difference between the two groups according to dyskinesia duration (P = 0.246), disability (P = 0.425) and early morning dystonia (P = 0.059), while there was significant improvement of painful dyskinesia in the study group (P = 0.046). Conclusions: The rTMS may have an additional therapeutic benefit for LID. Further studies may put the best rTMS parameters to establish more prominent and longer-lasting clinical effects. J Neurol Res. 2019;9(3):28-34 doi: https://doi.org/10.14740/jnr512
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".