{"id":"W3005033593","doi":"10.1002/nbm.4253","title":"Multi‐contrast volumetric imaging with isotropic resolution for assessing infarct heterogeneity: Initial clinical experience","year":2020,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; GE Healthcare","keywords":"Contrast (vision); Isotropy; Nuclear magnetic resonance; Resolution (logic); Materials science; Nuclear medicine; Medicine; Computer science; Physics; Artificial intelligence; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006449004,0.000982116,0.0006803095,0.0005826723,0.0002582173,0.0007295503,0.0009394819,0.001047623,0.001117489],"category_scores_gemma":[0.008433145,0.0002814413,0.0003108603,0.0004155878,0.001015016,0.0007071529,0.000584984,0.001066406,0.0005335082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003353169,"about_ca_system_score_gemma":0.0004758929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007435998,"about_ca_topic_score_gemma":0.0008223699,"domain_scores_codex":[0.9986879,0.0007414179,0.00009300931,0.0001993354,0.0001742435,0.0001040798],"domain_scores_gemma":[0.9975545,0.001182309,0.0001538536,0.0003619783,0.0003705816,0.0003768154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01482779,0.01233939,0.2113757,0.000962745,0.0005383649,0.01076664,0.003943234,0.00830113,0.09134811,0.002541533,0.003589125,0.6394663],"study_design_scores_gemma":[0.008422968,0.1170881,0.5873925,0.0008444748,0.0009515834,0.09272403,0.002360523,0.04417793,0.1032069,0.006221182,0.03591004,0.0006996117],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9534354,0.0105112,0.02866905,0.001149022,0.00009387475,0.0008013969,0.0001988369,0.0001373353,0.005003921],"genre_scores_gemma":[0.9604353,0.005163025,0.03210326,0.0007987198,0.000275685,0.0003560838,0.00031934,0.00007946099,0.0004691241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006449004,"threshold_uncertainty_score":0.03410596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1021874324984032,"score_gpt":0.4294567996982227,"score_spread":0.3272693671998195,"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."}}