Milestones in Parkinson's disease therapeutics
Bibliographic record
Abstract
In the mid-1980s, the treatment of Parkinson's disease was quite exclusively centered on dopatherapy and was focusing on dopamine systems and motor symptoms. A few dopamine agonists and a monoamine oxidase B inhibitor (selegiline) were used as adjuncts in advanced Parkinson's disease. In the early 2010s, levodopa remains the gold standard. New insights into the organization of the basal ganglia paved the way for deep brain stimulation, especially of the subthalamic nucleus, providing spectacular improvement of drug-refractory levodopa-induced motor complications. Novel dopamine agonists (pramipexole, ropinirole, rotigotine), catecholmethyltransferase inhibitors (entacapone), and monoamine oxidase B inhibitors (rasagiline) have also been developed to provide more continuous oral delivery of dopaminergic stimulation in order to improve motor outcomes. Using dopamine agonists early, before levodopa, proved to delay the onset of dyskinesia, although this is achieved at the price of potentially disabling daytime somnolence or impulse control disorders. The demonstration of an antidyskinetic effect of the glutamate antagonist amantadine opened the door for novel nondopaminergic approaches of Parkinson's disease therapy. More recently, nonmotor symptoms (depression, dementia, and psychosis) have been the focus of the first randomized controlled trials in this field. Despite therapeutic advances, Parkinson's disease continues to be a relentlessly progressive disorder leading to severe disability. Neuroprotective interventions able to modify the progression of Parkinson's disease have stood out as a failed therapeutic goal over the last 2 decades, despite potentially encouraging results with compounds like rasagiline. Newer molecular targets, new animal models, novel clinical trial designs, and biomarkers to assess disease modification have created hope for future therapeutic interventions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".