{"id":"W4387605246","doi":"10.21203/rs.3.rs-3215495/v1","title":"Image-based machine learning model as a tool for classification of [ 18 F]PR04.MZ PET images in patients with parkinsonian syndrome","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Movement Disorders","funders":"Agencia Nacional de Investigación y Desarrollo","keywords":"Artificial intelligence; Machine learning; Positron emission tomography; Computer science; Pet imaging; Pattern recognition (psychology); Medicine; Nuclear 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":[0.001663444,0.0006437647,0.0005346854,0.001293728,0.0002023058,0.001087821,0.0004601543,0.0009488703,0.000891314],"category_scores_gemma":[0.0039216,0.0001232906,0.0006402145,0.0004144076,0.0002362491,0.0003796497,0.0002647646,0.0004580329,0.0004422781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000600605,"about_ca_system_score_gemma":0.0004228675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003660153,"about_ca_topic_score_gemma":0.00199482,"domain_scores_codex":[0.9994991,0.000186671,0.00004910329,0.0001294265,0.00008381221,0.00005200635],"domain_scores_gemma":[0.9988095,0.0007052547,0.0001203019,0.00008591867,0.0002361465,0.00004300654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002824822,0.0008575642,0.2162869,0.0002177772,0.0006299099,0.0007867299,0.0003071283,0.1698062,0.02829182,0.0009632663,0.004116163,0.5749117],"study_design_scores_gemma":[0.0000278356,0.000274175,0.02847873,0.00002432026,0.00009140905,0.0002625276,0.0000662694,0.9634845,0.006336806,0.0004804227,0.0004489766,0.00002389777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8968096,0.001168622,0.09770346,0.0005034729,0.00008910379,0.000161277,0.0006834174,0.001181613,0.001699428],"genre_scores_gemma":[0.9762936,0.0001150204,0.02261599,0.00005380868,0.00001617179,0.0000542633,0.0004473121,0.0000171152,0.0003867206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003660153,"threshold_uncertainty_score":0.008797228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06798490694852795,"score_gpt":0.3760302764869122,"score_spread":0.3080453695383843,"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."}}