Impaired Pain Processing Correlates with Cognitive Impairment in Parkinson's Disease
Bibliographic record
Abstract
Objective Pain and cognitive impairment are important clinical features in patients with Parkinson's disease (PD). Although pain processing is associated with the limbic system, which is also closely linked to the cognitive function, the association between pain and cognitive impairment in PD is still not well understood. The aim of the study was to investigate the association between pain processing and cognitive impairment in patients with PD. Methods Forty-three patients with PD and 22 healthy subjects were studied. Pain-related somatosensory evoked potentials (SEPs) were generated using a thin needle electrode to stimulate epidermal Aδ fibers. Cognitive impairment was evaluated using the Mini-Mental State Examination (MMSE), the Frontal Assessment Battery, and Japanese version of the Montreal Cognitive Assessment (MoCA-J), and their correlation with pain-related SEPs was investigated. Results The N1/P1 amplitude was significantly lower in PD patients than the controls. N1/P1 peak-to-peak amplitudes correlated with the MMSE (r=0.66, p<0.001) and MoCA-J scores (r=0.38, p<0.01) in patients with PD. These amplitudes also strongly correlated with the domains of attention and memory in the MMSE (attention, r=0.52, p<0.001; memory, r=0.40, p<0.01) and MoCA-J (attention, r=0.45, p<0.005; memory, r=0.48, p<0.001), but not in control subjects. Conclusion A good correlation was observed between the decreased amplitudes of pain-related SEPs and an impairment of attention and memory in patients with PD. Our results suggest that pathological abnormalities of the pain pathway are significantly linked to cognitive impairment in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".