Adherence to Medication among Parkinson’s Disease Patients Using the Adherence to Refills and Medications Scale
Bibliographic record
Abstract
Objectives . Adherence to medication is an important factor that can influence Parkinson’s disease (PD) control. We aimed to explore patients’ adherence to antiparkinsonian medication and determine factors that might affect adherence to medications among PD patients. Methods . A cross‐sectional, exploratory survey of PD patients treated with at least one antiparkinsonian drug and with a total score of MoCA (Montreal Cognitive Assessment) ≥26 was conducted. The final sample included 112 PD patients. A patient’s adherence was assessed through ARMS (Adherence to Refills and Medications Scale). ARMS scores higher than 12 were assumed lower adherence. In addition, each patient underwent neurological examination, assessment of depression, anxiety, and evaluation of the presence of PD nonmotor symptoms. Results . The mean ARDS value in our cohort was 14.9 ± 2.5. Most PD patients (74.1%) reported lower adherence to their medication. Participants in the lower adherence group were younger at PD onset, had significantly higher UPDRS (Unified PD Rating Scale) scores, as well as UPDRS III and UPDRS IV subscores, HARS (Hamilton Anxiety Rating Scale), and NMSQuest (Non‐Motor Symptoms Questionnaire for PD) scores compared to the fully adherent group ( p = 0.013, p = 0.017, p = 0.041, p = 0.043, and p = 0.023, respectively). Among nonmotor PD symptoms, the presence of cardiovascular, apathy/attention‐deficit/memory disorders, hallucinations/delusions, and problems regarding changes in weight, diplopia, or sweating were associated with lower adherence. Multivariate regression analysis revealed depression as the strongest independent predictor of lower adherence. Conclusion . Depressed PD patients compared to PD patients without clinical depression had a three times higher risk for lower adherence to pharmacotherapy. Recognition and adequate treatment of depression might result in improved adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| 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) | 0.000 | 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 teacher head, 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".