New treatments for the motor symptoms of Parkinson’s disease
Bibliographic record
Abstract
Levodopa remains the most potent drug to treat motor symptoms in Parkinson's disease (PD); however, motor fluctuations and levodopa-induced dyskinesia that occur with long-term use restrict some of its therapeutic value. Despite these limitations, the medical treatment of PD strives for continuous relief of symptoms using different strategies throughout the course of the illness: increasing the half-life of levodopa, using 'levodopa-sparing agents' and adding non-dopaminergic drugs. New options to 'improve' delivery of levodopa are under investigation, including long-acting levodopa, nasal inhalation and continuous subcutaneous or intrajejunal administration of levodopa. Long-acting dopamine agonists were recently developed and are undergoing further comparative studies to investigate potential superiority over the immediate-release formulations. Non-dopaminergic drugs acting on adenosine receptors, cholinergic, adrenergic, serotoninergic and glutamatergic pathways are newly developed and many are being evaluated in Phase II and Phase III trials. This article focuses on promising novel therapeutic approaches for the management of PD motor symptoms and motor complications. We will provide an update since 2011 on new formulations of current drugs, new drugs with promising results in Phase II and Phase III clinical trials, old drugs with new possibilities and some new potential strategies that are currently in Phase I and II of development (study start date may precede 2011 but are included as study is still ongoing or full data have not yet been published). Negative Phase II and Phase III clinical trials published since 2011 will also be briefly mentioned.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".