{"id":"W4409313023","doi":"10.1038/s41598-025-96950-3","title":"Voice biomarkers as prognostic indicators for Parkinson’s disease using machine learning techniques","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Parkinson's disease; Disease; Computer science; Medicine; Machine learning; Bioinformatics; Data science; Internal medicine; Biology","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":[0.002007686,0.001060427,0.001029363,0.003204329,0.0002229599,0.00157542,0.0004067553,0.0008761066,0.0009679363],"category_scores_gemma":[0.00589299,0.000195236,0.0009903725,0.001592069,0.0002554388,0.0008683979,0.0005129573,0.001031936,0.0005649721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003009065,"about_ca_system_score_gemma":0.0003443982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001292933,"about_ca_topic_score_gemma":0.001173838,"domain_scores_codex":[0.9991627,0.0002820976,0.0001304536,0.0001648975,0.0001975403,0.00006238679],"domain_scores_gemma":[0.9969761,0.001746038,0.0005711264,0.0001343683,0.0004641589,0.0001083193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001379884,0.0005066317,0.4972121,0.0005058016,0.0008162531,0.000621726,0.0001857792,0.04000439,0.01121533,0.00112725,0.003762367,0.4426625],"study_design_scores_gemma":[0.00009981687,0.001394175,0.3264031,0.0005903853,0.0008393028,0.0017718,0.0005206263,0.643545,0.01169484,0.006783023,0.006171021,0.0001868488],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7828045,0.03687698,0.1644354,0.002260181,0.0006804797,0.0003735152,0.005767471,0.001373521,0.005428013],"genre_scores_gemma":[0.9668998,0.002660605,0.02731673,0.0001263535,0.0002477428,0.00009942472,0.001878181,0.0000220651,0.0007490931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003204329,"threshold_uncertainty_score":0.01061785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429336929075101,"score_gpt":0.3066950108793121,"score_spread":0.292401641588561,"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."}}