Improvement in Language Function Correlates with Gait Improvement in Drug-naïve Parkinson’s Disease Patients Taking Dopaminergic Medication
Bibliographic record
Abstract
BACKGROUND: Dopaminergic drugs, the gold standard for motor symptoms, are known to affect cognitive function in Parkinson's disease (PD) patients. OBJECTIVE: We compared the effects of dopaminergic treatment on motor and cognitive function in drug-naïve patients. METHODS: Dopaminergic medication (levodopa, dopamine agonist, selegiline) was given to 27 drug-naïve PD patients and increased to a dose optimal for improved motor symptoms. Patients were tested prior to, and 4-7 months after, drug initiation. Motor function was assessed using the Unified Parkinson's Disease Rating Scale (UPDRS). Cognitive function was assessed using both the Japanese version of the Montreal Cognitive Assessment (MoCA-J) and the Neurobehavioral Cognitive Status Examination (COGNISTAT-J). Improvements from baseline for both motor and cognitive assessment were compared. RESULTS: Mean score of all motor assessments (UPDRS total score of Parts II and III, and sub-scores of tremor, rigidity, bradykinesia, gait, and postural instability) and certain cognitive assessments (MoCA-J total score and subscore of delayed recall) significantly improved with dopaminergic medication. Gait score improvement showed significant positive correlation with improvement in MoCA-J language domain and in language-comprehension subtests of COGNISTAT-J using Spearman's correlation coefficients. Furthermore, multiple regression analysis showed gait score improvement significantly correlated with improvements in the subtests of language-comprehension in COGNISTAT-J. CONCLUSION: There is correlated improvement in both gait and language function in de novo PD patients in response to dopaminergic drugs. Gait and language dysfunction in these patients may share a common pathophysiology linked to dopamine deficits.
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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 | 0.002 |
| 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".