Effects of Cognitive Performance and Affective Status on Fatigue in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Fatigue is a common non-motor symptom in Parkinson's disease (PD) and is typically assessed via self-reported questionnaires such as the Parkinson's Fatigue Scale (PFS). The PFS captures the presence of subjective experience of physical fatigue as well as its impact on daily functioning. OBJECTIVES: We aimed to investigate whether different variables (cognition, neuropsychiatric symptoms, disease-related measures) are associated with the experience of physical fatigue in comparison to fatigue affecting daily functioning. METHOD: Sixty-two non-demented PD patients were evaluated through questionnaires assessing fatigue, daytime sleepiness, apathy, depression, anxiety, and cognition. Items of fatigue were classified and summarized into two index variables measuring either the subjective experience of physical fatigue or the impact of fatigue on daily functioning. Linear regression with a stepwise elimination procedure was conducted to select the significant predictors for each index variable separately. RESULTS: = 0.05). CONCLUSIONS: In conclusion, our work supports associations between fatigue and other neuropsychiatric symptoms in PD and extends prior work suggesting that motor disturbances are specifically linked to fatigue-related impairment of daily functioning, but not to the subjective experience of physical fatigue.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".