{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Benesh A"],"funding":["Tel Aviv University Center for AI and Data Science","Biogen","Israel Science Foundation"],"pagination":["46"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12910052"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_abstract":["Parkinson's disease (PD) is a highly heterogeneous condition with symptoms spanning motor and non-motor domains. Clinical scales like the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) are standard in clinical trials where disease progression is monitored. They rely on summing item values, assuming uniform item importance and score increments. Here, we propose a novel data-driven approach to optimize weights for such scales-so that total scores better reflect the underlying disease severity. In a retrospective observational analysis of longitudinal cohort data from the Parkinson's Progression Markers Initiative (PPMI), our methods identified which items (and value increments) most strongly indicate PD progression, down-weighting or excluding less informative items. The learned weights substantially improve the monotonic relationship between total scores and clinical progression. We validated our weights using both held-out PPMI data and an independent dataset (BeaT-PD), demonstrating their robustness. Applying such weights in clinical trials may increase power and reduce the required sample size<sup>1</sup>."],"journal":["NPJ Parkinson's disease"],"pubmed_title":["Optimizing Parkinson's disease progression scales using computational methods."],"pmcid":["PMC12910052"],"funding_grant_id":["2206/22"],"pubmed_authors":["Mirelman A","Benesh A","Shamir R","Alcalay RN"],"additional_accession":[]},"is_claimable":false,"name":"Optimizing Parkinson's disease progression scales using computational methods.","description":"Parkinson's disease (PD) is a highly heterogeneous condition with symptoms spanning motor and non-motor domains. Clinical scales like the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) are standard in clinical trials where disease progression is monitored. They rely on summing item values, assuming uniform item importance and score increments. Here, we propose a novel data-driven approach to optimize weights for such scales-so that total scores better reflect the underlying disease severity. In a retrospective observational analysis of longitudinal cohort data from the Parkinson's Progression Markers Initiative (PPMI), our methods identified which items (and value increments) most strongly indicate PD progression, down-weighting or excluding less informative items. The learned weights substantially improve the monotonic relationship between total scores and clinical progression. We validated our weights using both held-out PPMI data and an independent dataset (BeaT-PD), demonstrating their robustness. Applying such weights in clinical trials may increase power and reduce the required sample size<sup>1</sup>.","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-09T13:20:01.19Z","creation":"2026-07-09T13:10:10.775Z"},"accession":"S-EPMC12910052","cross_references":{"pubmed":["41577682"],"doi":["10.1038/s41531-026-01259-1"]}}