Effect of levodopa on frontal-subcortical and posterior cortical functioning in patients with Parkinson’s disease
Bibliographic record
Abstract
INTRODUCTION: Parkinson's disease (PD) is associated with cognitive decline but little is known about frontal-subcortical and posterior cortical cognitive functioning in patients with PD. The present study was designed to: (a) compare frontal and posterior cognitive functioning between patients with PD and healthy controls; (b) determine the effect of levodopa (L-dopa) on frontal-subcortical and posterior cortical cognitive functions; and (c) identify predictors of cognitive functions in patients with PD. METHODS: 50 patients diagnosed with PD from April 2016 to May 2017 at Civil Hospital, Bahawal Victoria Hospital, Bahawalpur, and Nishter Hospital Multan, Pakistan, and 50 healthy individuals from the community participated in our study. Patients had two testing sessions - first, at the time of diagnosis before taking L-dopa medication to determine baseline scores; and second, after at least three months of L-dopa medication. Participants completed the Parkinson's Disease-Cognitive Rating Scale. RESULTS: Patients with PD showed impaired performance on frontal-subcortical and posterior cortical functions in contrast with the control group. L-dopa medication had beneficial effects on frontal-subcortical and posterior cortical functioning in patients with PD. Disease duration was a significant predictor of cognitive performance in patients with PD. CONCLUSION: L-dopa medication improves frontal-subcortical and posterior cortical cognitive functioning in patients with PD. Disease duration is a marker of cognitive decline in PD.
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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 | 0.001 |
| 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 | 0.000 |
| 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".