Evaluating the differential effects of dopamine agonists and clonazepam on large muscle group movements in restless legs syndrome
Bibliographic record
Abstract
STUDY OBJECTIVES: This study aimed to investigate the effect of dopamine agonists (DA) and Clonazepam on Large Muscle Group Movements during sleep (LMM), a distinct motor phenomenon, in Restless Legs Syndrome (RLS). METHODS: A retrospective analysis was conducted on 51 drug-free adult patients with RLS, divided into three groups: 33 received a DA (pramipexole or ropinirole), 15 received clonazepam, and 18 received a placebo. Each patient underwent two consecutive nocturnal polysomnographic (PSG) recordings: one baseline and one following treatment administration. LMM and periodic leg movements during sleep (PLMS) were scored. Differences between groups were analyzed using ANCOVA, with the International Restless Legs Syndrome Study Group severity scale as a covariate, and within-group changes were assessed using paired t-tests. RESULTS: At baseline, no significant difference in LMM or PLMS was observed between groups. Following treatment, DA significantly reduced PLMS and periodicity indexes but did not significantly alter LMM indexes, except for a small increase in LMM duration. The placebo group exhibited a significant decrease in LMM index during the second night, potentially reflecting a first-night effect (FNE). Clonazepam did not significantly affect either PLMS or LMM. CONCLUSIONS: DA effectively reduced PLMS but did not significantly impact LMM, suggesting that LMM may involve different neurophysiological mechanisms. The potential FNE observed in the placebo group underscores the need for careful consideration of adaptation effects in sleep studies. Future research should explore alternative or adjunctive therapies targeting LMM and residual sleep disturbances in RLS.
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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) | 0.001 | 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".