{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002031244,0.0006352415,0.000330722,0.0003876657,0.0001918419,0.0007146604,0.0005311585,0.0007052889,0.003629492],"category_scores_gemma":[0.0008765582,0.0002624554,0.000267427,0.0003009037,0.000204841,0.0007484365,0.0005477789,0.0004288132,0.0008448496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001403072,"about_ca_system_score_gemma":0.0001954718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001107738,"about_ca_topic_score_gemma":0.001521117,"domain_scores_codex":[0.9999242,0.00001999638,0.000004823891,0.0000119897,0.00003049988,0.000008565565],"domain_scores_gemma":[0.999727,0.0001010405,0.00004179892,0.0000527708,0.00006156718,0.00001583621],"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":[0.000229162,0.0002256196,0.004434468,0.0002513601,0.00007370669,0.0007104349,0.0003004686,0.61935,0.1290275,0.01990915,0.004661315,0.2208268],"study_design_scores_gemma":[0.000003506318,0.00003170071,0.0007013806,0.000006645344,0.000005326292,0.00011463,0.00002370274,0.9911166,0.004992364,0.001534982,0.001463003,0.000006112614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04126332,0.0002466048,0.9526497,0.000111716,0.0000317162,0.00003388998,0.0002579504,0.000646243,0.004758989],"genre_scores_gemma":[0.6958118,0.000650297,0.2919281,0.0001295944,0.0001136957,0.0000766759,0.0006895319,0.0005389221,0.01006128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003629492,"threshold_uncertainty_score":0.01214188,"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."}}