Development and Validation of PARCOMS Composite Scales for Assessing Disease Progression and Treatment Effects in Parkinson’s Disease
Bibliographic record
Abstract
INTRODUCTION: Measures designed to comprehensively assess Parkinson's disease (PD) irrespective of disease stage and treatment status may be unable to capture nuances in disease progression, particularly in early-stage PD. The objective of this paper is to develop PARkinson's COMposite Scales (PARCOMS) with increased responsiveness to clinical decline using items of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) for three discrete cohorts of patients. METHODS: Patients with confirmed PD from the Parkinson's Progression Markers Initiative (PPMI) data were assigned to three cohorts based on use of dopaminergic treatment, stage of disease, and presence of motor complication. For each cohort, items from MDS-UPDRS Part I (PARCOMS-Non-Motor) and Parts II and III (PARCOMS-Motor) were selected based on responsiveness using partial least squares (PLS) regression. The responsiveness of the scales was estimated using mean-to-standard deviation ratios (MSDRs) of their change values. RESULTS: Compared to the original MDS-UPDRS, MSDRs for PARCOMS-Motor increased 13.1% (untreated cohort, n = 430), 78.2% (treated-without-motor-complications cohort, n = 426), and 100.6% (treated-with-motor-complications cohort, n = 538). The MSDR increases observed for PARCOMS-Non-Motor were 13.9%, 6.8%, and 20.7%, respectively. Across cohorts, turning in bed and speech items were large contributors to the PARCOMS-Motor scales. Items for cognitive impairment and urinary problems were substantial contributors to PARCOMS-Non-Motor across cohorts. There was variability in the weighting of items representing different clinical concepts across cohorts for each composite, confirming heterogeneity in disease progression across disease stages. CONCLUSIONS: PD stage-specific composite measures were developed and demonstrated greater sensitivity to change than the original MDS-UPDRS, supporting the value of weighted composites tailored for disease stage.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".