Impact of non-motor fluctuations on QOL in patients with Parkinson’s disease
Bibliographic record
Abstract
Introduction Long-term levodopa treatment in patients with Parkinson’s disease (PwPD) often causes motor fluctuations, which are known to affect their quality of life (QOL). These motor fluctuations may be accompanied by fluctuations in non-motor symptoms. There is no consensus on how non-motor fluctuations affect QOL. Methods This was a single-center, retrospective study and included 375 patients with Parkinson’s disease (PwPD) who visited the neurology outpatient department of Fukuoka University Hospital between July 2015 and June 2018. All patients were evaluated for age, sex, disease duration, body weight, and motor symptoms by the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale part III, depression scale by the Zung self-rating depression scale, apathy scale, and cognitive function by the Japanese version of The Montreal Cognitive Assessment. A nine-item wearing-off questionnaire (WOQ-9) was used to assess the motor and non-motor fluctuations. QOL in PwPD was investigated using the eight-item Parkinson’s Disease Questionnaire (PDQ-8). Results In total, 375 PwPD were enrolled and classified into three groups according to the presence or absence of motor and non-motor fluctuations. The first group included 98 (26.1%) patients with non-motor fluctuations (NFL group), the second group included 128 (34.1%) patients who presented with only motor fluctuations (MFL group), and the third group included 149 (39.7%) patients without fluctuations in motor or non-motor symptoms (NoFL group). Among them, the PDQ-8 SUM and SI were significantly higher in the NFL group than in the other groups ( p < 0.005), implying that the NFL group had the poorest QOL among groups. Next, multivariable analysis showed that even one non-motor fluctuation was an independent factor that worsened QOL ( p < 0.001). Conclusion This study showed that PwPD with non-motor fluctuation had a lower QOL than those with no or only motor fluctuation. Moreover, the data showed that PDQ-8 scores were significantly reduced even with only one non-motor fluctuation.
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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 | 0.002 |
| 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 | 0.000 | 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".