The role of opioids in restless legs syndrome: an [11C]diprenorphine PET study
Bibliographic record
Abstract
Opioids have been shown to provide symptomatic relief from dysaesthesias and motor symptoms in restless legs syndrome (RLS). However, the mechanisms by which endogenous opioids contribute to the pathophysiology of RLS remain unknown. We have studied opioid receptor availability in 15 patients with primary RLS and 12 age-matched healthy volunteers using PET and [11C]diprenorphine, a non-selective opioid receptor radioligand. Ligand binding was quantified by generating parametric images of volume of distribution (V(d)) using a plasma-derived input function. Statistical parametric mapping (SPM) was used to localize mean group differences between patients and controls and to correlate ligand binding with clinical scores of disease severity. There were no mean group differences in opioid receptor binding between patients and controls. However, we found regional negative correlations between ligand binding and RLS severity (international restless legs scale, IRLS) in areas serving the medial pain system (medial thalamus, amygdala, caudate nucleus, anterior cingulate gyrus, insular cortex and orbitofrontal cortex). Pain scores (affective component of the McGill Pain Questionnaire) correlated inversely with opioid receptor binding in orbitofrontal cortex and anterior cingulate gyrus. Our findings suggest that, the more severe the RLS, the greater the release of endogenous opioids within the medial pain system. We therefore discuss a possible role for opioids in the pathophysiology of RLS with respect to sensory and motor symptoms.
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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 | 0.000 |
| 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 | 0.001 | 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".