{"id":"W4403822130","doi":"10.1101/2024.10.27.24316215","title":"Prediction, prognosis and monitoring of neurodegeneration at biobank-scale via machine learning and imaging","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Biobank; Neurodegeneration; Scale (ratio); Artificial intelligence; Neuroscience; Computer science; Medicine; Psychology; Internal medicine; Cartography; Geography; Biology; Bioinformatics; Disease","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009060952,0.000721538,0.0007336141,0.002848452,0.000326555,0.001840699,0.0007661789,0.0009343121,0.0008063003],"category_scores_gemma":[0.02000377,0.0002465963,0.0005318712,0.001571517,0.0004806478,0.001295756,0.0008515121,0.0007991521,0.0004869776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006685854,"about_ca_system_score_gemma":0.0004857127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002842657,"about_ca_topic_score_gemma":0.002652142,"domain_scores_codex":[0.9974111,0.001329273,0.000298249,0.0006081047,0.0002436026,0.0001096832],"domain_scores_gemma":[0.9856933,0.006256181,0.004070011,0.001775315,0.001807583,0.0003976771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000561468,0.0001663444,0.9578171,0.00009439543,0.0005735229,0.0001062788,0.0000861886,0.00805298,0.00102453,0.0003236581,0.002595413,0.02859808],"study_design_scores_gemma":[0.000145745,0.000658942,0.8245155,0.0002443268,0.0008506798,0.0009406465,0.0002798876,0.1545943,0.006361786,0.007165744,0.004147937,0.00009454034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962394,0.002323451,0.02527757,0.001405651,0.00006876056,0.0001288393,0.006424286,0.0003915003,0.001585929],"genre_scores_gemma":[0.9855313,0.0003899387,0.01017276,0.0001706903,0.00007814176,0.00008444273,0.003306048,0.00001403219,0.0002526646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009060952,"threshold_uncertainty_score":0.04791945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712552511462045,"score_gpt":0.259801910472697,"score_spread":0.2426763853580766,"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."}}