Cognitive profiles at presentation and subsequent cognitive decline and quality of life in Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: Currently, our ability to predict cognitive decline in Parkinson's disease is limited. METHODS: In 234 dementia-free PD patients, cognitive profiles at baseline were determined using cluster analysis and longitudinal associations with global cognitive z-score, health-related quality of life (HR-QoL), caregiver burden, and risk of reliable cognitive change (RCC) were tested. RESULTS: Participants fell into four cognitive profiles at baseline: normal cognition (n = 108), memory impairment (n = 47), memory and executive function impairment (n = 59), and global cognitive impairment (n = 20). After adjusting for the severity of cognitive impairment, only the memory impairment cluster showed a higher risk of RCC. Baseline cognitive profiles did not predict different rates of cognitive decline, change in HR-QoL, or caregiver burden. DISCUSSION: The memory impairment cluster showed a higher risk of developing a reliable cognitive change; however, baseline cognitive profiles did not consistently predict different rates of cognitive decline, HR-QoL, or caregiver burden. HIGHLIGHTS: Data-driven approach to identify cognitive profiles. Cognitive decline across domains assessed by risk of reliable cognitive change. Cognitive profiles did not predict rate of cognitive decline. Memory impairment profile had a higher risk of reliable cognitive change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".