Self-Reported Nonadherence to Medication Is Not Associated with Health-Related Quality of Life in Parkinson’s Disease
Bibliographic record
Abstract
Nonadherence is a growing issue in the treatment of Parkinson’s disease (PD). Many factors are known to influence nonadherence, but little is known about the influence of quality of life (QoL). Detailed clinical data were obtained from 164 patients with PD using the Parkinson’s Disease Questionnaire-39 (PDQ-39) and the German Stendal Adherence with Medication Score (SAMS). Descriptive statistics were used to identify reasons for nonadherence, and multivariable linear models were used to study associations between QoL and clinical parameters as well as nonadherence. Multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA) were used to study the effect of the SAMS on PDQ domains and other medical covariates. The results showed that 10.4% (n = 17) of patients were fully adherent, 66.4% (n = 109) were moderately nonadherent, and 23.2% (n = 38) were nonadherent. Nonadherence was associated with male gender, lower Montreal Cognitive Assessment (MoCA) score, higher non-motor symptoms questionnaire (NMS-Quest) score, greater number of medications per day (an indicator of comorbidity), and higher Beck Depression Inventory (BDI) score. QoL was correlated with male gender, lower MoCA score, higher NMS-Quest score, more comorbidities, and higher BDI score, but was not correlated with nonadherence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".