Potentiation of response to low doses of levodopa in MPTP-injected monkeys by chemical unilateral subthalamotomy
Bibliographic record
Abstract
OBJECT: Subthalamotomy is a stereotactic surgery performed in patients with disabling dyskinesias due to Parkinson disease. The authors set out to model this human condition in MPTP monkeys and determine if subthalamotomy allowed a reduction of levodopa for similar benefit. METHODS: The authors performed unilateral subthalamotomy in 4 parkinsonian dyskinetic monkeys by stereotactic injection of ibotenic acid. An optimal dose, defined as the highest dose of levodopa improving parkinsonian motor symptoms while inducing low or no dyskinesias, was established in these animals. Each monkey was scored for the antiparkinsonian and dyskinetic effects of the optimal dose of levodopa, as well as suboptimal and dyskinesia-inducing doses (60% and 140% of the optimal dose, respectively), and these scores were compared with those obtained at baseline before and after subthalamotomy. Bradykinesia was assessed by a prehension task. RESULTS: Unilateral subthalamotomy had a positive effect on the antiparkinsonian response for all doses of levodopa as well as the baseline. There were no differences in the antiparkinsonian response between the suboptimal dose postsurgery and the optimal dose presurgery. Dyskinesias were increased at the suboptimal and the optimal doses. After surgery, the duration of response to levodopa increased between 20% and 25% in the suboptimal dose, whereas it remained unchanged with higher doses. Bradykinesia was significantly reduced after surgery only at the suboptimal dose. CONCLUSIONS: Subthalamotomy potentiated the response to suboptimal doses of levodopa. Thus, levodopa can be reduced by 40% after surgery for similar beneficial antiparkinsonian response and less dyskinesia than with an optimal dose before surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".