{"id":"W3127318101","doi":"10.2196/22164","title":"Identifying Myocardial Infarction Using Hierarchical Template Matching–Based Myocardial Strain: Algorithm Development and Usability Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Economic and Social Research Council; Chief Scientist Office, Scottish Government Health and Social Care Directorate; Scottish Government; Health and Social Care Research and Development Division; Medical Research Council; Public Health Agency; University of Warwick; Department of Health and Social Care; British Heart Foundation; Wellcome Trust","keywords":"Myocardial infarction; Magnetic resonance imaging; Medicine; Receiver operating characteristic; Algorithm; Cardiac magnetic resonance; Diastole; Radial stress; Cardiac magnetic resonance imaging; Cardiology; Infarction; Internal medicine; Radiology; Computer science; Deformation (meteorology); Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01371825,0.0007656253,0.0007164171,0.00161864,0.0002834185,0.001184063,0.001227194,0.0008243195,0.001355233],"category_scores_gemma":[0.04114709,0.0002807618,0.0008120141,0.001112499,0.0002892196,0.001052366,0.001037678,0.0004826318,0.000408756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005665033,"about_ca_system_score_gemma":0.0009232967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003371102,"about_ca_topic_score_gemma":0.002704298,"domain_scores_codex":[0.9941505,0.003579163,0.0005978279,0.0007312314,0.0007956753,0.0001456912],"domain_scores_gemma":[0.9770722,0.01675106,0.0007781637,0.001401389,0.003800061,0.0001971347],"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":[0.001833169,0.001893413,0.08528604,0.0006008272,0.0007165552,0.0003337967,0.0009271653,0.05160006,0.01536688,0.001383561,0.002426665,0.8376318],"study_design_scores_gemma":[0.000303592,0.001850928,0.02447969,0.0000734502,0.0002547683,0.0005663631,0.0003245134,0.959372,0.01012017,0.0008542336,0.001750101,0.0000501611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5921912,0.000799721,0.4012741,0.0002250372,0.00007397978,0.001517782,0.0004058885,0.001944048,0.001568297],"genre_scores_gemma":[0.606307,0.000300181,0.3912151,0.00006283549,0.00002065844,0.0006653348,0.000803933,0.0001392724,0.0004857153],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01371825,"threshold_uncertainty_score":0.07255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03130257879547738,"score_gpt":0.333834785764835,"score_spread":0.3025322069693576,"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."}}