Demographic Influences on the Relationship Between Fatigue and Quality of Life in Parkinson's Disease
Bibliographic record
Abstract
Abstract Background Fatigue has a major impact on health‐related quality of life (HR‐QOL) in Parkinson's disease (PD). Objectives To determine whether demographic characteristics modify the relationship between fatigue and HR‐QOL. Methods Patients with PD in the Fox Insight study completed the Parkinson Fatigue Scale (PFS‐16) and Geriatric Depression Scale (GDS‐15). Linear regression examined the relationship between the PFS‐16 and Parkinson Disease Quality of Life, as modified by age, sex, and GDS‐15. Results A total of 1029 participants (44% female, mean age 67.4 years, and mean disease duration 4.6 years) were included in this analysis. Multivariable regression modeling demonstrated a negative effect modification for age (β = −0.07, P < 0.001) and a positive effect modification for the GDS‐15 (β = 0.057, P = 0.002), but not for sex (β = −0.021, P = 0.231). Conclusion The association between fatigue and worse HR‐QOL is greater at younger ages and in individuals with more depressive symptoms. Targeted therapeutics for these individuals may provide the greatest impact on fatigue in 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 | 0.001 | 0.007 |
| 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) | 0.002 | 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".