Development of composite scales for assessing disease progression and treatment effects among patients with Parkinson's disease in a clinical trial setting
Bibliographic record
Abstract
BACKGROUND: The measures used to assess Parkinson's disease (PD) in clinical trials were developed for a broad spectrum of disease severity, limiting their ability to detect meaningful changes in early PD over a feasible study period. OBJECTIVE: To develop PD composite scales (PARCOMS) using clinical trials data with increased responsiveness to clinical decline in patients with early untreated disease. METHODS: Subjects from the placebo arms of clinical trials (Critical Path for Parkinson's [CPP] dataset), diagnosed with PD within the previous two years, with no current or prior use of dopaminergic therapies were included. Partial least squares (PLS) regression was used to develop two composite scales: PARCOMS-Function using items from the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part II and Parkinson's Disease Questionnaire (PDQ-39) scales, and PARCOMS-Motor using items from MDS-UPDRS Parts II and III. Scale responsiveness was estimated using mean to standard deviation ratios (MSDRs) for change from baseline at 12-months. RESULTS: The MSDR for PARCOMS-Function (n = 140) was 12.3 % higher vs MDS-UPDRS Part II alone and 339 % higher vs PDQ-39 alone. The MSDR for PARCOMS-Motor (n = 181) was 27.5 % higher vs the combined MDS-UPDRS Parts II and III. PARCOMS-Function retained 15-items (34.1 %) from Part II and PDQ-39. PARCOMS-motor retained 23-items (50.0 %) from Part II and III. Items that were not responsive to change or that were correlated with more responsive items were omitted. CONCLUSIONS: Clinical trial data were used to develop two PARCOMS scales which demonstrated greater sensitivity to disease progression in patients with early PD over 12-months compared to the original scales.
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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 | 0.086 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".