Effect of motor, non-motor clinical features including sleep quality, and prescription pattern on adherence to antiparkinsonian medications in Parkinson’s disease
Bibliographic record
Abstract
Objectives: Adherence to antiparkinsonian medications (APMs) may significantly influence Parkinson’s disease (PD) outcome. The present study assesses the role of motor and non-motor features, and prescription patterns on adherence. Materials and Methods: This observational and cross-sectional study included 50 PD patients taking APMs for ≥24 months. Demographic data, PD characteristics, treatment, and follow-up history were collected. Patients following up at least once in six months were considered as regular, else were labeled irregular. Montreal cognitive assessment, patient health questionnaire-4, Pittsburgh sleep quality (SQ) index, Epworth sleepiness scale, global quality of life (GQOL) scale, and Morisky Green Levine medication adherence scale (MGL-MAS) were used to evaluate cognition, depressive and anxiety features, SQ, excessive daytime sleepiness (EDS), quality of life (QOL), and APMs adherence, respectively. Results: Nearly half (46%) of the PD patients reported high adherence (MGL-MAS = 0). Most of the clinical characteristics were comparable between those with medium/low and high adherence, except for a larger proportion of patients in the medium/low adherence group belonging to Hoehn–Yahr stage >2 ( P = 0.02). A comparable proportion of patients in both groups reported poor SQ ( P = 0.52) and EDS ( P = 0.32). In comparison to the high adherence group, a significantly lower median GQOL score was observed in the medium/low adherence group (median [interquartile range] = 65 [50–70] vs. 80 [70–85]; P < 0.001). The APMs prescription and follow-up patterns were comparable between both groups. Conclusion: More than half the PD patients reported medium-to-low adherence. While motor severity and depressive symptoms were associated with medium-to-low adherence, poor SQ was comparable in both groups. Those with medium-to-low adherence reported poor QOL.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".