Impact of Cognitive Functioning and Age on Patient-Reported Outcomes in Parkinson’s Disease
Bibliographic record
Abstract
Abstract Cognitive impairment is prevalent in Parkinson Disease (PD) and increasing age is a PD risk factor. Age and cognition may impact patient-reported outcome measures (PROMs) level, reliability, or validity of responses. This study investigated the relative impact of cognitive function and age on PROMs in PD. Cross-sectional data (n=676) included assessments of age, cognition (Montreal Cognitive Assessment; MoCA) and PROMIS-29 Profile (physical functioning, anxiety, depression, fatigue, sleep disturbance, social functioning, pain). Analyses examined differences by age and MoCA in: 1)Level—correlations, multivariable regressions controlling for disease severity (UPDRSmotor, PD duration), comorbidity (CIRS-G), demographics; 2)Reliability--Cronbach’s alpha, and 3)Validity--correlations of PROMIS physical function with physician assessments. Sample was age M=68.0(SD=9.1); range=36-93 years, 64% male, 87% white, 37% college educated, PD duration M=8.2(SD=6.1) years, and MoCA M=24.3(SD=4.9; range 2-30). Greater cognitive impairment was consistently associated with greater physical/mental impairment (r=.14-.45; p<.05), except for sleep disturbance (r=-.07, p=.08) Multivariable regressions found cognition remained a significant predictor of physical functioning, anxiety, and depression older age predicted anxiety and social functioning. Comorbidity was the greatest predictor across all the PROMs (r=.22-.45). Reliability for PROMIS measures was excellent (alpha>.8) across cognitive and age groups, except for Fatigue at MoCA.36) across cognition and age groups. Cognitive impairment in PD is associated with lower physical function and mental health levels. Reliability and validity of most PROMs in PD are neither impacted by cognition nor age.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".