Predictors of deterioration in health‐related quality of life in Parkinson's disease: Results from the DATATOP trial
Bibliographic record
Abstract
The aim of this study was to investigate factors associated with decline in health-related quality of life in Parkinson's disease, by a retrospective cohort study from referral centers in Canada and the United States. Subjects were patients with early Parkinson's disease (N = 362) enrolled in a clinical trial of deprenyl (selegiline) and tocopherol (DATATOP) and followed prospectively. The main outcome measure was change in health-related quality of life using SF-36 Mental and Physical Component Summary scores. The mean interval between SF-36 measurements was 1.7 +/- 0.1 years, beginning 5 to 6 years after enrolment into the trial. In multivariable analysis, baseline Hamilton Depression Scale scores and self-rated cognitive function were associated with subsequent decline in Physical Component Summary scores, while older age and Schwab and England activities of daily living scores were associated with decline in Mental Component Summary scores. The Postural Instability Gait Disorder score was the only variable found to decline concurrently with HRQOL. Our results suggest that depression, self-rated cognitive function, and one's degree of functional independence are predictors of subsequent changes in HRQOL. Our focus in clinical care needs to be broadened beyond assessing and treating Parkinsonism, recognizing the impact of mood, cognition and function on HRQOL.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".