Emerging drugs for levodopa-induced dyskinesia
Bibliographic record
Abstract
INTRODUCTION: Levodopa continues to be the main symptomatic therapy for clinical features of Parkinson's disease. However, prolonged use leads to motor complications, including levodopa-induced dyskinesia (LID). This has debilitating impact on the patients and is a significant challenge for the treating physician. There are currently limited pharmacological options for reducing established LID without causing side effects. Drugs to prevent or delay LID are also an increasing part of the strategy to manage LID, but have yet to show promise. Agents that allow levodopa to be used effectively, without inducing LID, are the goal of current research strategies. AREAS COVERED: LID occurs due to significant modifications in the basal ganglia circuitry, probably related to the chronic, pulsatile stimulation of striatal dopaminergic receptors by levodopa, as well as altered non-dopaminergic neurotransmitter system signaling pathways. Novel treatments that either result in continuous dopaminergic receptor stimulation, levodopa 'sparing strategies' or non-dopaminergic targets, including glutamatergic, serotonergic, adenosine, adrenergic and cholinergic neurotransmission are thus the main treatment options and the focus of this manuscript. Randomized controlled trials in progress (ClinicalTrials.org) or recently published articles are included. EXPERT OPINION: The success of future therapeutic approaches will depend on the potential success of translational research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".