Chronic levodopa therapy does not improve skilled reach accuracy or reach range on a pasta matrix reaching task in 6‐OHDA dopamine‐depleted (hemi‐Parkinson analogue) rats
Bibliographic record
Abstract
L-dopa therapy reverses some but not all of the motor deficits in human Parkinson patients. Although a number rat analogues of human Parkinson's disease have been developed for evaluating the efficacy of drug therapies, it is not known whether L-dopa has a similar selective action on the motor symptoms in the rat models. To examine the effectiveness of L-dopa in reversing the motor deficits in rats, we administered 6-OHDA unilaterally to produce hemi-Parkinson rats, which were then trained to reach for food using either their impaired (contralateral to the lesion) limb or their good (ipsilateral to the lesion) limb. To assess the skill, accuracy and range of limb movement, rats reached for pasta from a horizontal array of 260 vertically orientated pieces of pasta. The number and location of pasta pieces taken from this matrix was calculated and the qualitative aspects of the reaching movements were rated. The quantitative data on pasta sticks retrieved indicated that forelimb extension and movement radius around the shoulder joint was reduced by 6-OHDA treatment and did not improve after chronic L-dopa treatment. The qualitative analysis showed that grasping patterns, paw movements and body movements impaired by the lesion were also not improved by L-dopa treatment. These findings are the first in the rat to suggest that whereas L-dopa has a general activating effect on the rat's whole-body movements, as displayed in contralateral rotation, its effectiveness does not extend to skilled forelimb movements. The results are discussed in relationship to the idea that the restoration of some skilled movements may require normal synaptic function.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".