Primate Models of Parkinson's Disease
Bibliographic record
Abstract
An adequate animal model of a disease should reproduce the cardinal features of its human counterpart. Parkinson’s disease is characterized by akinesia, rigidity, and tremor. Attempts to reproduce Parkinsonian symptoms can be divided in three broad categories according to the method used. Earlier studies were based mostly on destruction of certain brain structures by surgical lesions with or without the help of stereotaxy. Then, with development of our knowledge of neurotransmitters, Parkinsonian features could be reproduced by drugs that affect more or less selectively certain biogenic amines. More recently, the serendipitous discovery of 1 methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) provided us with the best model of Parkinson’s disease to date. We will review these models,which have evolved over a period of over fifty years and have provided us with powerful tools to understand the pathophysiology of Parkinson’s disease and to test new therapeutic approaches. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".