{"id":"W2548620780","doi":"10.1002/jmri.25512","title":"Clinical evaluation of three‐dimensional late enhancement MRI","year":2016,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Circle Cardiovascular Imaging","funders":"Engineering and Physical Sciences Research Council; British Heart Foundation; Wellcome Trust","keywords":"Medicine; Magnetic resonance imaging; Radiology; Nuclear medicine; Image quality; Cardiac magnetic resonance; Gradient echo; Diagnostic accuracy; Angiology; Cardiology; Computer science; Image (mathematics)","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":[0.00101052,0.0003621393,0.0003272188,0.0009512343,0.0001369616,0.0006214076,0.0003002142,0.000458138,0.001249605],"category_scores_gemma":[0.009705927,0.0001465254,0.0001634703,0.0003244526,0.0004758886,0.0004883003,0.0003684489,0.0002068149,0.0004260399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659071,"about_ca_system_score_gemma":0.0001749673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002986755,"about_ca_topic_score_gemma":0.00026727,"domain_scores_codex":[0.9994055,0.0001864023,0.0001006249,0.0001250891,0.0001271441,0.00005524237],"domain_scores_gemma":[0.9961984,0.000772607,0.001749135,0.000166318,0.0006821732,0.0004313666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005409964,0.00005692997,0.982863,0.00004888379,0.00004034449,0.001529387,0.00009095675,0.0002036951,0.004743054,0.0000275923,0.0001694615,0.009685787],"study_design_scores_gemma":[0.00004871634,0.0009213734,0.984902,0.00003578898,0.00004407997,0.01080187,0.0001055602,0.001230661,0.001440399,0.00005253531,0.0003981715,0.00001886269],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977581,0.0005852372,0.0006081611,0.00005521273,0.000006065803,0.0000300615,0.0000861598,0.00001970119,0.0008513673],"genre_scores_gemma":[0.9991758,0.0001167033,0.0004636963,0.00002871553,0.00001444926,0.00001212517,0.0001076751,0.000002577995,0.00007826943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001249605,"threshold_uncertainty_score":0.005344212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03268406429805493,"score_gpt":0.3524368238613041,"score_spread":0.3197527595632492,"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."}}