Relationship between Motor and Nonmotor Symptoms and Quality of Life in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Background: Parkinson’s disease (PD) is a chronic neurodegenerative disease that implies a progressive and invalidating functional organic disorder, which continues to evolve till the end of life and causes different mental and physical alterations that influence the quality of life of those affected. Objective: To determine the relationship between motor and nonmotor symptoms and the quality of life of persons with PD. Methods: An analytic, descriptive, cross-sectional study was conducted with patients with different degrees of PD in the Albacete Health district. The estimated sample size required was 155 patients. The instruments used for data collection included a purpose-designed questionnaire and “Parkinson’s Disease Questionnaire” (PDQ-39), which measures eight dimensions and has a global index where a higher score indicates a worse quality of life. A descriptive and bivariate analysis was conducted (SPSS® IBM 24.0). Ethical aspects: informed consent and anonymized data. Results: A strong correlation was found between the number of motor and nonmotor symptoms and global health-related quality of life and the domains mobility, activities of daily living, emotional well-being, cognitive status, and pain (p < 0.05). Receiving pharmacological treatment and taking more than four medicines per day was significantly associated with a worse quality of life (p < 0.05). Patients who had undergone surgical treatment did not show better global quality of life (p = 0.076). Conclusions: All nonmotor symptoms and polypharmacy were significantly associated with a worse global quality of life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".