Levodopa‐induced dyskinesias
Bibliographic record
Abstract
Levodopa-induced dyskinesias (LID) are common and difficult to treat. This review focuses on three issues related to LID: clinical features, classification and rating, pathophysiology and pathogenesis, and management. The three primary clinical syndromes are OFF-period dystonia, peak-dose dyskinesia, and diphasic dyskinesia. Several other forms also occur, making the evaluation and choice of treatment complicated. A core component of the pathophysiology of LID is overactivity of the direct striatal output pathway. This pathway provides a direct GABAergic connection by which the striatum inhibits the output regions of the basal ganglia, i.e., the internal globus pallidus and the substantia nigra pars reticulata. Altering dopaminergic dosing and timing can abate dyskinesias, but usually impact the control of parkinsonism. Putative therapies to reduce the problem of dyskinesias could focus on the glutamatergic, GABAergic, alpha2 adrenergic, serotonergic (5HT1A, 5HT2A), opioid, histamine H3, adenosine A2A receptors, the monoamine transport or cannabinoid CB1 receptors systems. The only currently available drug with an evidence-based recommendation on efficacy for dyskinesia is amantadine. Therapy goals include the prevention of dyskinesia and treatment of dyskinesias that are troublesome clinically. New rating measures to assess severity and disability related to dyskinesia are in the process of development and clinimetric testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".