Do dyskinesia and pain share common pathophysiological mechanisms in Parkinson's disease?
Bibliographic record
Abstract
Plastic changes within the striatum resulting from pulsatile dopaminergic stimulation are thought to lead to dyskinesia in patients with Parkinson's disease (PD). The basal ganglia play a role in processing pain. We hypothesized that the plastic changes that lead to dyskinesia may also mediate pain responses. Our objective was to compare the change in pain sensitivity after levodopa administration between stable responders, fluctuators without dyskinesia, and dyskinetic patients, and to compare pain sensitivity between PD and healthy subjects. Fifty patients with PD were assessed with cold water immersion after overnight withdrawal of dopaminergic medications and again after a standard levodopa challenge, and carefully classified into stable responder (n = 12), fluctuator (n = 15), and dyskinetic (n = 23) groups. Twenty age-matched controls were also tested. PD patients "off"-medication had a lower threshold (P = 0.016) and tolerance (P < 0.0001) to cold pain compared to controls. After levodopa administration, dyskinetic patients experienced a large increase in cold pain threshold (48%) and tolerance (66%) that was absent in stable responders (P = 0.038 and P = 0.015); there was no significant difference in pain sensitivity change scores between the fluctuator and either the stable responder or dyskinetic groups. Our results suggest that dyskinesia and pain may share common pathophysiological mechanisms in PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".