{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002823799,0.0001632053,0.0004163508,0.000281685,0.0000734959,0.00002750426,0.0001012698,0.00007656918,0.00007271404],"category_scores_gemma":[0.001431736,0.0001302794,0.0001112297,0.000733338,0.0002612313,0.0001538093,0.00005194646,0.0002393334,0.000008456201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008131185,"about_ca_system_score_gemma":0.0001070269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008875157,"about_ca_topic_score_gemma":0.00004629781,"domain_scores_codex":[0.9983169,0.00005666432,0.0006549981,0.0004048871,0.0002557504,0.0003107626],"domain_scores_gemma":[0.9989365,0.0003116395,0.0001354715,0.0002075639,0.000159151,0.0002497041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006171064,0.0005132011,0.9200802,0.0001466954,0.000039601,0.0002540483,0.0003442584,0.000009445737,0.002551125,0.00001282715,0.0008112331,0.07462019],"study_design_scores_gemma":[0.01364616,0.002408172,0.9110261,0.0008423602,0.0001603021,0.0001242185,0.001142802,0.06040369,0.0003305358,0.000006397663,0.009692935,0.0002163148],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9048295,0.0009878941,0.08762572,0.0054754,0.0002798385,0.000629287,0.00001938458,0.00006705521,0.00008594435],"genre_scores_gemma":[0.9802875,0.00006845182,0.01622446,0.002564071,0.0006617388,0.00006237474,0.00008913229,0.0000232389,0.00001905557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.075458,"threshold_uncertainty_score":0.5312641,"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."}}