A-47Neuropsychiatric, Motor, and Demographic Correlates of Apathy in Parkinson's Disease
Bibliographic record
Abstract
Objective: To understand the relationship between apathy and mood, cognition, psychosis, behavioral regulation, sleep, motor functioning, and demographic factors in Parkinson's disease (PD). Method: A sample of 111 participants with PD followed in neurology clinics at a tertiary medical center were administered the Apathy Scale (AS), Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), Montreal Cognitive Assessment (MoCA), Frontal Systems Behavior Scale (FrSBe), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Epworth Sleepiness Scale (ESS), and Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) as part of a larger prospective longitudinal investigation of neuropsychiatric symptoms in PD. Demographic information and data from formal assessment of psychosis were also collected. A one-way multivariate analysis of variance for parametric, and chi-square test for nonparametric variables were conducted to assess for significant differences between low (AS = 13) and high (AS > 13) apathy groups. Results: There were significant differences (p < .05) between low and high apathy groups for the BDI-II, BAI, MoCA, FrSBe Executive Dysfunction subscale, MDS-UPDRS Part III, and education, but not the FrSBe Disinhibition subscale, RBDSQ, ESS, current psychosis, age, sex, or disease duration (see Table 1). Mood measures yielded the largest effect sizes (BDI-II ƞ2 = .22; BAI ƞ2 = .21), followed by education (ƞ2 = .12), FrSBe Executive Dysfunction (ƞ2 = .07), MoCA (ƞ2 = .06), and MDS-UPDRS Part III (ƞ2 = .05). Conclusion: Mood, cognition, aspects of behavioral regulation, motor functioning, and education are associated with apathy in PD. These results further the understanding of neuropsychiatric, motor, and demographic correlates of apathy in this clinical population. Additional study of these constructs may help direct treatment of individuals with PD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".