Quality of life in early Parkinson's disease: Impact of dyskinesias and motor fluctuations
Bibliographic record
Abstract
The impact of dyskinesias and motor fluctuations on quality of life (QOL) at various stages in the course of Parkinson's disease (PD) is not well understood. In 301 subjects with early PD enrolled in a clinical trial (CALM-PD), we quantified the impact of motor complications on QOL and investigated how this changes over time. We also compared QOL related to demographic and treatment characteristics. The presence of dyskinesias was associated with visual analogue scale (VAS) scores 3.0 of 100 points higher (better) than those without dyskinesias in years 1 to 2, even when adjusting for Unified Parkinson's Disease Rating Scale (UPDRS) motor scores. The positive association between dyskinesias and QOL scores was more marked in older patients. In years 3 to 4, dyskinesias no longer had a significant relationship with QOL. Younger subjects had higher VAS scores. Gender, motor fluctuations, and treatment regimen had no significant association with QOL, although a trend was found toward a small negative effect of motor fluctuations on QOL. We conclude that motor complications that occur within the first 4 years of treatment of PD do not have a significant negative effect on quality of life as measured by a visual analogue scale for most 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".