How effective are treatment guidelines for augmented RLS?
Bibliographic record
Abstract
STUDY OBJECTIVES: The objective of this study was to assess the effectiveness of current treatment guidelines for restless legs syndrome (RLS) augmentation in patients on dopamine agonists (DAs) which recommend a cross-titration strategy to an alpha-2-delta ligand (A2D) and/or opioid. METHODS: Consecutive new consultations for RLS with both augmentation and active treatment with DAs at the time of initial assessment were included if followed >5 months. Clinical information from the semi-structured initial consultation, and subsequent visits until their most recent/final visit was extracted. Clinical Global Impression-Severity (CGI-S) and Clinical Global Impression-Improvement (CGI-I) scores were retrospectively determined by two independent evaluators. RESULTS: In the 63 patients with augmented RLS on DAs, followed for 5-59 months (mean = 28, SD = 14), the average age was 67.6 (SD = 9.8) and 63% were female. Mean duration of prior dopaminergic therapy was 11.6 years (SD = 6.7) and average pramipexole equivalent dose was 1.23 mg (SD = 1.22 mg). At baseline, RLS was "moderate-markedly" severe (CGI-S = 4.9). At the final/most recent visit, 78% (49/63) were classified as Responders (CGI-I ≤ 2, "Much" or "Very Much Improved") with an average CGI-S of 2.4 ("borderline-mildly ill"). Responders (59%) were more likely to have discontinued DAs than Non-Responders (40%), and mean opioid doses were higher in Responders (39 vs 20 MME). No differences in baseline DA dose, final A2D dose, or iron therapy were observed between groups. Responders did have significantly more severe RLS, more sleep maintenance insomnia, and greater subjective daytime sleepiness at baseline (p < 0.05). CONCLUSIONS: Guideline-based management is effective in most patients with augmented RLS on DAs.
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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 | 0.009 | 0.059 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".