{"id":"W4402023869","doi":"10.1016/j.nuclcard.2024.101966","title":"Training and Validating a Neural Network for Myocardial Blood Flow Mapping in 82Rb PET: A Multicenter Study","year":2024,"lang":"en","type":"article","venue":"Journal of Nuclear Cardiology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Montreal Heart Institute; Ottawa Hospital; Micropharma (Canada)","funders":"","keywords":"Medicine; Cerebral blood flow; Multicenter study; Cardiology; Blood flow; Internal medicine; Medical physics; Nuclear medicine; Randomized controlled trial","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":[0.009053702,0.001276424,0.001029169,0.000440338,0.000506527,0.0007190378,0.00150152,0.001439039,0.0009404958],"category_scores_gemma":[0.009269502,0.0004899919,0.0008094816,0.0003912711,0.0007665652,0.0008360023,0.0009009371,0.0008770737,0.0004785686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006966505,"about_ca_system_score_gemma":0.001062264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007215108,"about_ca_topic_score_gemma":0.005391708,"domain_scores_codex":[0.9979544,0.001089987,0.0001593812,0.0005020879,0.000167583,0.0001264563],"domain_scores_gemma":[0.9945771,0.002748167,0.0004017743,0.00083481,0.001220543,0.0002176864],"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":[0.01729999,0.01411171,0.2731744,0.0005579755,0.00328444,0.0009667919,0.001230381,0.2232861,0.05007256,0.000557834,0.005713587,0.4097442],"study_design_scores_gemma":[0.00132966,0.01328983,0.1159326,0.000106436,0.001488275,0.0008788991,0.0007090737,0.8347707,0.02871512,0.0004888453,0.002186263,0.0001041899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908824,0.0003138353,0.007958261,0.00007647982,0.00003229114,0.000138475,0.0002239684,0.0001071694,0.0002672047],"genre_scores_gemma":[0.9853045,0.0001714888,0.01231093,0.00007129081,0.00002432892,0.0002100498,0.001192569,0.00005286009,0.0006619776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009053702,"threshold_uncertainty_score":0.04788113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05391870904462755,"score_gpt":0.3342322762917705,"score_spread":0.280313567247143,"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."}}