Identification of Daily Activity Impairments in the Diagnosis of Parkinson Disease Dementia
Bibliographic record
Abstract
OBJECTIVE: We studied activities of daily living (ADL) in Parkinson disease (PD) to identify the cognitive ADL impairments that could differentiate patients with PD dementia from those without dementia. BACKGROUND: Most people with PD have impairments in their ADL, making it difficult to distinguish between those caused by cognitive or motor dysfunction. METHODS: We evaluated 24 patients with PD dementia and 48 with PD without dementia. For comparison, we evaluated 24 patients with Alzheimer disease and 25 healthy control participants. Caregivers completed the instrumental ADL scale, allowing us to examine participants' actual activity (actual score) and cognitive ability to perform certain ADL (cognitive score). RESULTS: The nondemented patients with PD had better actual scores than those with dementia. The patients with PD dementia had significantly worse cognitive scores for keeping appointments and for talking about recent events, followed by managing money, using a telephone, and cooking. A comparison of the actual and cognitive scores revealed significant differences between the two PD groups, suggesting the physical impact of PD on certain ADL. Factor analysis confirmed that ADL items could be separated into cognitive and physical components. CONCLUSIONS: Although most patients with PD had difficulties in ADL, we identified specific cognitive ADL items that could help in differentiating patients with and without dementia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".