{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001604143,0.0001432481,0.0002495751,0.0002534712,0.00008745932,0.00001526116,0.00006457163,0.0001148843,0.0003442336],"category_scores_gemma":[0.00005853207,0.0001560204,0.0002238536,0.0004332024,0.00006947831,0.0002280452,0.00004685598,0.0001011879,0.0002912204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001061384,"about_ca_system_score_gemma":0.00007340316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005397081,"about_ca_topic_score_gemma":0.000004008158,"domain_scores_codex":[0.998812,0.0000350837,0.0004475186,0.0002631258,0.0002275259,0.0002147273],"domain_scores_gemma":[0.9991394,0.00005798413,0.0001546765,0.0002894767,0.00028506,0.00007342616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009930449,0.0002542667,0.02982262,0.0002371427,0.00009482414,0.00004392632,0.0005062043,0.002220858,0.947575,0.0004850187,0.004891928,0.01376885],"study_design_scores_gemma":[0.001287507,0.0001458078,0.9016931,0.0001750091,0.0001576444,0.0001103836,0.0002993206,0.07903717,0.01342031,0.002895554,0.0005944651,0.0001837388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759833,0.0002951408,0.0212113,0.0003862064,0.0001426868,0.0004217413,0.00004094571,0.0001820101,0.001336627],"genre_scores_gemma":[0.996043,0.00006844324,0.001931341,0.00009672185,0.00007225712,0.00002123947,0.0009429056,0.00003263078,0.0007914191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9341547,"threshold_uncertainty_score":0.6362326,"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."}}