{"id":"W4406972484","doi":"10.1016/j.jocmr.2024.101229","title":"Inline strain analysis for DENSE CMR","year":2025,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Siemens (Canada)","funders":"","keywords":"Medicine; Angiology; Strain (injury); Internal medicine","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":[0.0005911087,0.0001184979,0.000532768,0.0002571455,0.00003582686,0.00004124379,0.0001687928,0.00007247031,0.00001169939],"category_scores_gemma":[0.0001217405,0.0001102803,0.000995749,0.0004122907,0.00002314095,0.0000741674,0.00001648436,0.0001247814,0.000001151233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004059031,"about_ca_system_score_gemma":0.00003537927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003607773,"about_ca_topic_score_gemma":0.000003965166,"domain_scores_codex":[0.9990122,0.00003644316,0.0004348964,0.0001017626,0.0002332945,0.0001814373],"domain_scores_gemma":[0.9994088,0.00007076581,0.00003975924,0.0002320614,0.0001991437,0.00004951226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006038184,0.00001700533,0.00006809999,0.000177359,0.002979437,0.00003371397,0.00005442207,0.7368177,0.001491497,0.0004986138,0.0003209977,0.2574808],"study_design_scores_gemma":[0.002146985,0.0001861458,0.008269312,0.0002508007,0.006418316,0.00004652111,0.00005494699,0.1826375,0.002238262,0.001908637,0.7955279,0.0003146744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1448165,0.2408796,0.6129428,0.00005174956,0.000658138,0.0001443227,0.00002525849,0.00003145623,0.0004502021],"genre_scores_gemma":[0.9699796,0.008510409,0.02082493,0.00005814235,0.0003727683,0.00001320057,0.000003029605,0.00002419648,0.0002137164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8251631,"threshold_uncertainty_score":0.4497099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008172265778752451,"score_gpt":0.2111116563000148,"score_spread":0.2029393905212624,"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."}}