Prodromal Marker Progression in Idiopathic Rapid Eye Movement Sleep Behavior Disorder: Sample Size for Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: To estimate prodromal marker progression in idiopathic rapid eye movement sleep behavior disorder patients with prodromal Parkinson's disease (PD) to calculate sample size for neuroprotective trials. METHODS: Patients with polysomnogram-proven idiopathic rapid eye movement sleep behavior disorder were assessed for prodromal PD using Movement Disorder Society criteria. We prospectively measured progression rates of numerous clinical variables, including motor, cognitive, special sensory, and autonomic variables and calculated the sample size required to demonstrate slowing of progression under 3 effectiveness assumptions (30%, 50%, and 70% slowing). RESULTS: Overall, the variables that progressed with lowest sample size requirements were motor variables (234 participants required per group for 50% efficacy over 2 years). By contrast, cognitive, special sensory, and autonomic variables showed modest progression with high variability, resulting in high sample sizes. The most efficient design was a time-to-event analysis using milestones of motor and cognitive decline (126 per group). CONCLUSION: In idiopathic rapid eye movement sleep behavior disorder, time-to-event analysis assessing milestones of decline is the most efficient trial design for neuroprotective therapy. © 2019 International Parkinson and Movement Disorder Society.
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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 | 0.171 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".