Dual-Task Performance in GBA Parkinson’s Disease
Bibliographic record
Abstract
INTRODUCTION: (GBA-PD) show faster motor and cognitive decline than idiopathic Parkinson's disease (iPD) patients, but the mechanisms behind this observation are not well understood. Successful dual tasking (DT) requires a smooth integration of motor and nonmotor operations. This study compared the DT performances between GBA-PD and iPD patients. METHODS: Eleven GBA-PD patients (p.N370S, p.L444P) and eleven matched iPD patients were included. Clinical characterization included a motor score (Unified PD Rating Scale-III, UPDRS-III) and nonmotor scores (Montreal Cognitive Assessment, MoCA, and Beck's Depression Inventory). Quantitative gait analysis during the single-task (ST) and DT assessments was performed using a wearable sensor unit. These parameters corrected for UPDRS and MoCA were then compared between the groups. RESULTS: Under the DT condition "walking while checking boxes," GBA-PD patients showed slower gait and box-checking speeds than iPD patients. GBA-PD and iPD patients did not show significant differences regarding dual-task costs. CONCLUSION: This pilot study suggests that DT performance with a secondary motor task is worse in GBA-PD than in iPD patients. This finding may be associated with the known enhanced motor and cognitive deficits in GBA-PD compared to iPD and should motivate further studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".