Embedding Patient Input in Outcome Measures for Long‐Term Disease‐Modifying Parkinson Disease Trials
Bibliographic record
Abstract
BACKGROUND: Clinical trials of disease-modifying therapies in PD require valid and responsive primary outcome measures that are relevant to patients. OBJECTIVES: The objective is to select a patient-centered primary outcome measure for disease-modification trials over three or more years. METHODS: Experts in Parkinson's disease (PD), statistics, and health economics and patient and public involvement and engagement (PPIE) representatives reviewed and discussed potential outcome measures. A larger PPIE group provided input on their key considerations for such an endpoint. Feasibility, clinimetric properties, and relevance to patients were assessed and synthesized. RESULTS: Although initial considerations favored the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III in Off, feasibility, PPIE input, and clinimetric properties supported the MDS-UPDRS Part II. However, PPIE input also highlighted the importance of nonmotor symptoms, especially in the longer term, leading to the selection of the MDS-UPDRS Parts I + II sum score. CONCLUSIONS: The MDS-UPDRS Parts I + II sum score was chosen as the primary outcome for large 3-year disease-modification trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of 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 | 0.689 | 0.777 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".