{"id":"W4382795576","doi":"10.1111/micc.12820","title":"Extended‐volume image‐derived models of coronary microcirculation","year":2023,"lang":"en","type":"article","venue":"Microcirculation","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Victoria University; Victoria University of Wellington; Fondation Leducq; University of Auckland; Royal Society Te Apārangi","keywords":"Microvessel; Computer science; Segmentation; Volume (thermodynamics); Microcirculation; Pipeline (software); Coronary arteries; Artificial intelligence; Computer vision; High resolution; Biomedical engineering; Image processing; Pattern recognition (psychology); Image (mathematics); Artery; Medicine; Pathology; Geology; Physics; Radiology; Cardiology; Remote sensing","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":[0.0002527485,0.0004836447,0.0003485067,0.0006213851,0.00009554275,0.0006552179,0.0006163242,0.0004904564,0.001042918],"category_scores_gemma":[0.0005037183,0.0002359731,0.0006084948,0.0004501106,0.0001844086,0.0003984276,0.0003851481,0.0004689198,0.00029826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004362939,"about_ca_system_score_gemma":0.0003764008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001797343,"about_ca_topic_score_gemma":0.002459021,"domain_scores_codex":[0.9999119,0.00001434352,0.000005321469,0.00002531596,0.00003140061,0.00001170726],"domain_scores_gemma":[0.9997851,0.0000815831,0.00004362471,0.00003582667,0.00003626452,0.00001759536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003508487,0.0001649964,0.003500475,0.0003588731,0.0001692076,0.0003492024,0.000126864,0.6818355,0.2692676,0.004001839,0.002101077,0.03777352],"study_design_scores_gemma":[0.000009674612,0.00005859418,0.002742991,0.000009355625,0.00001699544,0.0001139188,0.00001278851,0.9818259,0.01259512,0.001306771,0.001293596,0.00001412168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3409195,0.00109722,0.6504493,0.0002318042,0.0000577348,0.0001212859,0.003547312,0.001764944,0.001810962],"genre_scores_gemma":[0.7431702,0.001379663,0.2468038,0.00009508852,0.00006221018,0.0003083102,0.005775047,0.0003779113,0.002027804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001797343,"threshold_uncertainty_score":0.003573716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03016478390482848,"score_gpt":0.2898658367664039,"score_spread":0.2597010528615754,"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."}}