P.042 Associations of pain and depression with marital status in patients diagnosed with Parkinson’s disease
Bibliographic record
Abstract
Background: Depression and pain are significant clinical problems that are comorbid with Parkinson’s disease (PD). However, the relationship of these variables with the marital status of patients with PD has not been explored in previous studies. The goal of this study was to assess the possible relationship between depression prevalence, depression severity, and pain interference with the marital status of the sufferers of PD. Methods: This study included 40 patients and 40 healthy control participants who were assessed for depression prevalence and pain interference using The Hospital Anxiety and Depression Scale and the Brief Pain Inventory, respectively. Results: When compared to the control groups, the PD (Single) group was found to have the highest prevalence of depression, followed by the PD (Married) group whereas the Control (Single) group was found to have a higher prevalence than the Control (Married) group (P<0.0001). A main effect was found on depression severity (P<0.0001), but no significant differences were observed between the PD groups. Lastly, PD (Single) patients had significantly greater pain interference scores than the PD (Married) patients (P<0.05) with no other significant case-control or control-control group differences. Conclusions: Patient-spouse relationship may have a mitigating effect on patient outcomes of depression prevalence and pain interference.
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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 | 0.003 |
| 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) | 0.006 | 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".