Mood, side of motor symptom onset and pain complaints in Parkinson's disease
Bibliographic record
Abstract
OBJECTIVE: Patients with Parkinson's disease (PD) present with a variety of non-motor symptoms including sensory complaints and mood disturbances. In the current pilot study, we aimed to explore pain complaints and the association between mood and pain in PD. We hypothesized that pain ratings would be elevated in patients with PD relative to controls. As PD is lateralized at onset and studies have found lateralization of some non-motor symptoms in PD, we also hypothesized that PD patients would exhibit differing pain profiles depending on side of onset of the disease. METHODS: Twenty-three PD patients (11 right-onset (RPD), 12 left-onset (LPD) disease), and 11 control participants (CS) completed a mood questionnaire (Depression Anxiety Stress Scale; DASS) as well as the short form of the McGill Pain Questionnaire. RESULTS: Both PD groups reported higher present pain intensity scores (p = 0.001), more evaluative pain intensity (p = 0.02), and more overall pain (p = 0.02) than control participants. There was a significant association between mood and all of the McGill pain ratings in the LPD patients, with those reporting more mood symptoms rating higher on all pain scales (all p-values < 0.001). This association was not found in the RPD group. CONCLUSIONS: Our results suggest an association between mood and pain in patients with PD that may be related to the differential contribution of right-hemispheric neural networks in processing of mood and pain states. These findings merit further investigation into the relation between mood and pain in patients with 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 | 0.000 | 0.001 |
| 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".