{"id":"W4412837543","doi":"10.1101/2025.07.31.25332494","title":"Optimizing Parkinson’s Disease progression scales using computational methods","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Disease; Parkinson's disease; Computer science; Neuroscience; Medicine; Psychology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005274465,0.0018641,0.001530386,0.001214026,0.0004485287,0.001733641,0.001634352,0.001833542,0.001845451],"category_scores_gemma":[0.01817861,0.0009298619,0.001260352,0.0009602733,0.001019618,0.001195522,0.001235481,0.002743396,0.0004309523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001470959,"about_ca_system_score_gemma":0.002019761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008160153,"about_ca_topic_score_gemma":0.007833403,"domain_scores_codex":[0.9982912,0.0009195456,0.00008217608,0.0004035203,0.0002079588,0.0000956399],"domain_scores_gemma":[0.9895999,0.008596717,0.0004777382,0.0004190788,0.0006647861,0.0002417493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001196136,0.0001570918,0.003195439,0.00009879273,0.0001129996,0.00004263643,0.00002854357,0.9508094,0.0005319549,0.002767158,0.002980237,0.03915604],"study_design_scores_gemma":[0.00001825364,0.00001397852,0.0001893666,0.000008089559,0.000005716652,0.000004478869,0.000002969105,0.9968609,0.00009513199,0.002641844,0.0001559493,0.00000343587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1342206,0.002380786,0.8533214,0.002678791,0.0002632067,0.0003131073,0.001366267,0.001965849,0.003490019],"genre_scores_gemma":[0.6284089,0.0004043474,0.3624996,0.001202594,0.0002325852,0.0006564032,0.003201851,0.0004299078,0.002963771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008160153,"threshold_uncertainty_score":0.02789432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0559608990169803,"score_gpt":0.3988459333156621,"score_spread":0.3428850342986818,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}