Negative impact of severity of pain on mood, social life and general activity in Parkinson’s disease
Bibliographic record
Abstract
OBJECTIVE: Pain is an important non-motor symptom of Parkinson's disease (PD); however, it remains understudied. The purpose of previous studies on the relationship between PD and pain, has been to explore the cause, origin and types of pain. This case control study is designed for clinicians and rehabilitation specialists to effectively identify pain from the patient's point of view. Pain present in PD patients correlates with significant disruption to their daily lives, which was seen by analysing characteristics, frequency, severity and interference of pain. METHOD: A total of 100 PD patients and 100 control healthy individuals, consisting of 66 males and 34 females were evaluated during routine clinical assessment followed by a neurological exam. The Brief Pain Inventory (BPI) was used to measure chronic pain in terms of pain severity, pain interference and pain frequency between the two groups. RESULTS: It was determined that PD patients had significantly higher pain severity scores compared to controls (p < 0.05). PD patients with depressive symptoms had significantly higher pain severity and pain interference scores than controls without depressive symptoms. PD patients reported greater scores on Global BPI pain interference and all components of the pain interference subscale. DISCUSSION: PD and depression seem to be correlated with higher perceived pain, severity and interference. These findings have not been reported by other case control studies, and warrant further causal research into pain, depression and PD.
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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 | 0.000 |
| 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) | 0.001 | 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".