Effects of pain on activities of daily living and functioning in Parkinson’s disease patients
Bibliographic record
Abstract
In parkInson's dIsEasE patIEnts abstract objective:To investigate how pain affects Parkinson's disease (PD) patients in their activities of daily living (ADL) and functioning, and to identify correlations among ADL and types of pain, pain locations, and demographics.Methods: The study population comprised 104 PD patients who reported feeling pain.Patients indicated whether pain affected particular actions, including standing, walking, and laboring.Patients further reported the functional effects of pain on their emotional and social health.Data was collected using self-made questionnaires.results: Overall, akathisic pain was the most effective predictor of negative effects of pain on ADL.The presence of akathisic pain increased the likelihood of mobility problems by almost 4.0-fold (p = 0.024), exhaustion by 10.0-fold (p = 0.017), social functioning problems by 3.5-fold (p = 0.037), lack of enjoyment in life by approximately 4.0-fold (p = 0.017) and problems in emotional functioning by 3.6-fold (p = 0.025).In addition, it was found that the duration of PD was correlated to problems with daily work (p = 0.033); Hoehn and Yahr stage of PD at diagnosis was correlated to exhaustion (p = 0.013); and male gender was correlated to a lack of perceived enjoyment of life (p = 0.010).conclusion: Pain is a debilitating symptom of PD patients, and has a negative impact on ADL.Thus, medical professionals should seriously consider the effects of pain in deciding the optimum therapeutic approach for PD patients.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".