N-Methyl-D-Aspartate Antagonists in Levodopa Induced Dyskinesia: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Levodopa-induced dyskinesias (LID) are amongst the most disabling side-effects of levodopa therapy for Parkinson's disease (PD). It has been suggested that that N-Methyl-D-Aspartate (NMDA)-receptor antagonist may reduce peak-dose dyskinesia in PD patients and may lead to motor improvement. In this study, we compared the efficacy of NMDA receptor antagonists versus placebo in the treatment of LID in PD through a meta-analysis of controlled trials. METHODS: Electronic search of Pubmed (1990 - 2010), Medline (1966-2010), EMBASE (1974-2010) and other databases for relevant studies were performed. Controlled clinical trials of the effects of NMDA antagonists on LID that fulfill the study protocol were selected. Pooled data from included studies was then used to perform random and fixed effect models meta-analysis. RESULTS: The search resulted in 11 randomized, placebo controlled clinical trials that involved a total of 253 PD patients with peak-dose LID. The outcome measures were various dyskinesia rating scales and the Unified Parkinson Disease Rating Scale (UPDRS) subscales III and IV. The analysis showed significant reduction in Standard Mean Difference (SMD) for UPDRS IV (SMD -1.45; 95%CI -2.28 to -0.63) and UPDRS III (SMD -0.41; 95%CI -0.69 to -0.12) after treatment with amantadine. Other included drugs did not show significant change in the outcomes measured. CONCLUSION: This meta-analysis provides an update on the clinical trials and confirms the short-term benefits of amantadine therapy in the treatment of dyskinesia. The effects of other NMDA receptor antagonists need to be evaluated further in clinical trials.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.057 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".