{"id":"W2922233872","doi":"10.1007/s11307-019-01334-5","title":"Artificial Neural Network–Based Prediction of Outcome in Parkinson’s Disease Patients Using DaTscan SPECT Imaging Features","year":2019,"lang":"en","type":"article","venue":"Molecular Imaging and Biology","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation of Sri Lanka; Michael J. Fox Foundation for Parkinson's Research","keywords":"Parkinson's disease; Medicine; Artificial neural network; Spect imaging; Nuclear medicine; Radiology; Computer science; Artificial intelligence; Disease; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009946433,0.0001680868,0.0002863402,0.000145674,0.00003986135,0.00001334566,0.00004661515,0.00003027768,0.00001591398],"category_scores_gemma":[0.00002896876,0.0001427682,0.00008337593,0.0001236789,0.00006194725,0.00004431184,0.00004348044,0.0001071871,0.000001726367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005212612,"about_ca_system_score_gemma":0.00003936306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001560099,"about_ca_topic_score_gemma":0.000004599421,"domain_scores_codex":[0.9988949,0.00008988583,0.0002554778,0.0003689073,0.00009559888,0.0002952352],"domain_scores_gemma":[0.9995164,0.00001629128,0.00009697202,0.0002054459,0.00003515411,0.0001297456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002803227,0.0001686113,0.9827777,0.00003438483,0.00002361036,0.0001021576,0.000009911033,0.0002308153,0.01020309,0.00004300314,0.00001485347,0.006111612],"study_design_scores_gemma":[0.001569773,0.00008269118,0.9492798,0.00007494025,0.0001284663,0.000009282925,0.00001259815,0.0469724,0.001094769,0.0002390994,0.0004254171,0.000110716],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976681,0.001011621,0.0002961084,0.0002459635,0.000280609,0.000330819,0.00007932686,0.00002772243,0.00005966885],"genre_scores_gemma":[0.9987054,0.00000613812,0.0004190842,0.0005494401,0.00004943039,0.000007258296,0.0002396721,0.00001841478,0.00000519681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04674158,"threshold_uncertainty_score":0.5821919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919597000483511,"score_gpt":0.278723355301394,"score_spread":0.2595273852965588,"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."}}