Apathy Profile in Parkinson’s and Huntington’s Disease: A Comparative Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND/AIMS: Apathy is one of the most frequent, disabling and difficult-to-treat symptoms that show up in many neurodegenerative disorders. The aim of this study was to assess and compare apathy profile in Parkinson's and Huntington's patients using the same comprehensive instruments to measure apathy, cognition and depressive symptoms. MATERIALS AND METHODS: We consecutively assessed Parkinson's disease (PD) and Huntington's disease (HD) patients recruited from a Movement Disorders Unit. In all patients, information related to demographics, clinical data, motor score (Movement Disorders Society-Unified Parkinson Disease Rating Scale; Unified Huntington Disease Rating Scale), cognition (Montreal Cognitive Assessment scale), depressive symptoms (Beck Depression Inventory II) and apathy (Apathy Evaluation Scale - clinical version) was collected. Patients with dementia or major depression according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revised criteria were excluded from the study. RESULTS: Seventy-five patients were enrolled, 45 with PD and 30 with HD. Apathy was present in 42.5% of PD patients and 51.7% of HD patients. In PD patients, apathy was associated with motor score, shorter duration of disease, lower dose of levodopa equivalent daily dose and depressive symptomatology, whereas in HD patients, apathy was related to disease duration, motor score and cognitive impairment. CONCLUSIONS: We found a similar prevalence of apathy in PD and HD patients but with different clinical correlations and different apathy domains involved, and this may warrant the development of different therapeutic approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".