Osteoarthritis Increases Paresthestic and Akathisic Pain, Anxiety Case-ness, and Depression Severity in Patients With Parkinson's Disease
Bibliographic record
Abstract
Objective: Parkinson’s disease (PD) patients are known to suffer from pain, anxiety and depression, but the exact degree of association between the two is unknown. As many PD patients also suffer from physical impairments, this cross-sectional case-control study sets out to compare and determine the caseness of pain, anxiety and depression in PD patients that suffer with or without symptomatic osteoarthritis (OA). The goal of this study, therefore, was to observe if additional pain associated with comorbid OA in PD patients is correlated with greater depression and anxiety rates. The importance of understanding the pain burden and increased depression severity of PD and OA patients is so they can be screened appropriately based on the symptoms, increasing their overall quality of life. Methods: This cross-sectional case-control study included 3 groups of 34 patients and 78 healthy age and gender-matched control participants. PD patients with symptomatic OA (PD+OA), PD patients without symptomatic OA (PD), patients with symptomatic OA but no PD (OA), and healthy control participants (Control). A PD patient group with Restless Legs Syndrome (PD+RLS) of 27 patients was also included. All participants completed questionnaires to assess for pain, depression and anxiety. Results: PD+OA and PD patients had worsened depression severity and were more likely to report anxiety and depression case-ness than OA patients. PD+OA patients were more likely to complain about paresthestic and akasthisic pain, but less likely to complain about aching pain compared to PD patients and OA patients. PD+OA patients were more likely to have greater pain severity, and were more likely to report radiating and sharp pain than PD+RLS patients. PD+OA patients were also more likely to report higher depression case-ness than PD+RLS patients. Conclusion: PD with OA seems to be linked with specific pain characteristics (akathisia and paraesthesia) as well as heightened overall pain severity and pain interference in comparison to OA alone, PD alone and PD with RLS. PD is also correlated with depression severity and anxiety case-ness in OA when compared to the OA alone, PD alone and PD with RLS.
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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 | 0.001 | 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".