Clinical characteristics of fatigued Parkinson’s patients and the response to dopaminergic treatment
Bibliographic record
Abstract
BACKGROUND: Fatigue, which is commonly observed in Parkinson's disease (PD), can greatly reduce quality of life and is difficult to treat. We here aimed to investigate the prevalence and characteristics of fatigue among PD patients and to explore an effective strategy to treat PD fatigue. METHOD: This was an observational cross-sectional study conducted in northeastern China. We examined fatigue in 222 PD patients from northeastern China using the Parkinson Fatigue Scale-16 (PFS-16). The disease severity, depression, sleep and cognitive functioning were assessed with the Hoehn & Yahr staging (H-Y stage), Unified Parkinson's Disease Rating Scale (UPDRS), Hamilton Depression Scale (HAMD), Parkinson's Disease Sleep Scale (PDSS) and Montreal Cognitive Assessment (MoCA) by interview. RESULTS: The frequency of fatigue in PD patients was 59.46 %. Fatigued patients had longer disease durations and greater disease severity than nonfatigued patients. Additionally, fatigued PD patients scored significantly higher for all motor symptoms, except for tremor, and had more serious depressive symptoms and sleep disturbances than nonfatigued PD patients did. The sleep disturbance severity was an independent factor for fatigue. Furthermore, 43.04 % of fatigued patients taking dopaminergic drugs had fatigue remission. Depression severity was identified as an independent factor for dopaminergic drug non-responsive fatigue. CONCLUSIONS: PD patients with severe sleep disturbances tend to suffer from fatigue. Levodopa improved fatigue only in PD patients with mild depression or no depression, implying that dopaminergic medication is required, but not sufficient, for fatigue suppression in PD patients with moderate or severe depression. Thus, restoring serotonergic neurotransmission as a combination therapy may offer a better strategy for the treatment of fatigue in these patients.
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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 | 0.001 |
| 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".