Chronic treatment with small doses of cabergoline prevents dopa‐induced dyskinesias in parkinsonian monkeys
Bibliographic record
Abstract
Levodopa continues to be the most effective agent for the symptomatic treatment of Parkinson's disease (PD). But over time, initial benefits decline in efficacy because of a rise in adverse effects such as dyskinesias. The pathophysiology of levodopa-induced dyskinesias (LID) is not completely understood, but it appears to result from deficient regulation by dopamine of corticostriatal glutamatergic inputs leading to a cascade of neurochemical changes in the striatum and the output pathways. In the present study, we examined if the addition of small doses of cabergoline (a long-acting D(2) receptor agonist) to levodopa could prevent LID. The major hypothesis is that sustained activation of postsynaptic D(2) receptors on medium spiny neurons even by small doses of cabergoline could prevent or reduce LID. The minor hypothesis, and the more controversial of the two, is that the long-acting stimulation by small doses of cabergoline could diminish the release of glutamate by the corticostriatal pathway and prevent LID. Eight MPTP-treated monkeys with a long-standing and stable parkinsonian syndrome and having never received dopaminergic agents were used. Two groups of four were treated for 1 month with levodopa/benserazide administered orally (100 mg/25 mg). The second group received in addition a threshold dose of cabergoline (dose ranging from 0.015 to 0.035 mg/kg, SC). During the treatment, we observed LID in the levodopa group but not in the group receiving levodopa+cabergoline. Furthermore, the combination produced a comparable antiparkinsonian effect in terms of quality but prolonged the duration (by 1 to 2 hours) and increased the locomotion (mean for 2 weeks congruent with 104%). Our data suggest that a small dose of a long-acting D(2) agonist combined with high doses of levodopa could be preventive of LID in patients with PD and could be an alternative to using antiglutamatergic agents for this purpose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".