Nondopaminergic Treatments for Parkinson’s Disease: Current and Future Prospects
Bibliographic record
Abstract
Parkinson's disease is primarily caused by dysfunction of dopaminergic neurons, however, nondopaminergic (ND) systems are also involved. ND targets are potentially useful to reduce doses of levodopa or to treat nonlevodopa-responsive symptoms. Recent studies have investigated the role of ND drugs for motor and nonmotor symptoms. Adenosine A2A receptor antagonists, mixed inhibitors of sodium/calcium channels and monoamine oxidase-B have recently been found to improve motor fluctuations. N-methyl-d-aspartate receptor antagonists and serotonin 5HT1B receptor agonists demonstrated benefit in levodopa-induced dyskinesia. Conversely, studies using antiepileptic drugs and adrenoreceptor antagonist had conflicting results. Moreover, metabotropic glutamate receptor antagonists also failed to improve symptoms. The current review summarizes the most recent findings on ND drugs over the last 2 years.
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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 | 0.001 | 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) | 0.008 | 0.002 |
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".