Which dyskinesia scale best detects treatment response?
Bibliographic record
Abstract
Numerous scales assess dyskinesia in Parkinson's disease (PD), variably focusing on anatomical distribution, phenomenology, time, severity, and disability. No study has compared these scales and their relative ability to detect change related to an established treatment. We conducted a randomized placebo-controlled trial of amantadine, assessing dyskinesia at baseline and at 4 and 8 weeks using the following scales: Unified Dyskinesia Rating Scale (UDysRS), Lang-Fahn Activities of Daily Living Dyskinesia Rating Scale (LF), 26-Item Parkinson's Disease Dyskinesia scale (PDD-26), patient diaries, modified Abnormal Involuntary Movements Scale (AIMS), Rush Dyskinesia Rating Scale (RDRS), dyskinesia items from the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), and Clinical Global Impression (severity and change: CGI-S, CGI-C). Scale order was randomized at each visit, but raters were aware of each scale as it was administered. Sensitivity to treatment was assessed using effect size. Sixty-one randomized dyskinetic PD subjects (31 amantadine, 30 placebo) completed the study. Four of the 8 scales (CGI-C, LF, PDD-26, and UDysRS) detected a significant treatment. The UDysRS Total Score showed the highest effect size (η(2) = 0.138) for detecting treatment-related change, with all other scales having effect sizes < 0.1. No scale was resistant to placebo effects. This study resolves 2 major issues useful for future testing of new antidyskinesia treatments: among tested scales, the UDysRS, having both subjective and objective dyskinesia ratings, is superior for detecting treatment effects; and the magnitude of the UDysRS effect size from amantadine sets a clear standard for comparison for new agents.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".