{"id":"W6889032915","doi":"10.25345/c5kn57","title":"MassIVE MSV000087016 - Plasma proteomics of ischemic and nonischemic cardiomyopathy patients","year":2021,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cardiomyopathy; Proteomics; Ischemic cardiomyopathy; Heart failure; Disease","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002824457,0.001024082,0.001625228,0.0004370314,0.0001238369,0.0001007193,0.0008367064,0.0009024985,0.0006170144],"category_scores_gemma":[0.0006020125,0.001056534,0.0003710218,0.0005366553,0.0004603337,0.0002254494,0.001138822,0.00105062,0.001035008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002926949,"about_ca_system_score_gemma":0.0004992117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00016967,"about_ca_topic_score_gemma":0.0001086335,"domain_scores_codex":[0.9956381,0.0002088355,0.001007253,0.001346849,0.0009888074,0.0008101718],"domain_scores_gemma":[0.9959519,0.0001611335,0.001201635,0.001863698,0.000495269,0.0003263683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001875501,0.0002665236,0.001446915,0.0009806084,0.000458968,0.0001155449,0.00006168593,0.000003856103,0.01026076,0.000001071511,0.986084,0.0001324517],"study_design_scores_gemma":[0.002305508,0.0001574397,0.0002548937,0.0007857812,0.0006383658,0.00003857939,0.0000899067,0.00001300468,0.04537878,0.000007141104,0.9492499,0.001080759],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1433016,0.0007238994,0.000001542925,0.00000565579,0.0004156037,0.001187866,0.8536094,0.00006281798,0.0006916253],"genre_scores_gemma":[0.002812934,0.0002316778,0.0007836642,0.00003877336,0.0002834905,0.0002453788,0.9951981,0.0002557534,0.0001502815],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1415887,"threshold_uncertainty_score":0.9997428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008343562151446039,"score_gpt":0.2241346602616521,"score_spread":0.2157910981102061,"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."}}