{"id":"W4392112240","doi":"10.1016/j.ejrad.2024.111386","title":"Reproducibility assessment of rapid strains in cardiac MRI: Insights and recommendations for clinical application","year":2024,"lang":"en","type":"article","venue":"European Journal of Radiology","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDI Integrated Facility Services (Canada)","funders":"","keywords":"Reproducibility; Medicine; Intraclass correlation; Feature tracking; Generalizability theory; Repeatability; Limits of agreement; Cardiac imaging; Cardiac magnetic resonance; Nuclear medicine; Concordance correlation coefficient; Linear regression; Magnetic resonance imaging; Bland–Altman plot; Coefficient of variation; Cardiac magnetic resonance imaging; Radiology; Cardiology; Statistics; Artificial intelligence; Pattern recognition (psychology); 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":[0.1794947,0.001808494,0.003788856,0.002961131,0.001018339,0.005108831,0.005252006,0.003829808,0.0009536589],"category_scores_gemma":[0.2998691,0.000816225,0.001834802,0.002403181,0.003534022,0.003533783,0.002471358,0.002725793,0.0009650026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002581233,"about_ca_system_score_gemma":0.00638426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004903416,"about_ca_topic_score_gemma":0.005938786,"domain_scores_codex":[0.8714705,0.07524472,0.01643803,0.006353243,0.02952756,0.0009659962],"domain_scores_gemma":[0.6285945,0.203511,0.03456992,0.0301095,0.1000661,0.003149036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001082154,0.0004395477,0.2818108,0.006536704,0.001295474,0.0003406696,0.00205006,0.00505311,0.006034529,0.007235357,0.0294683,0.6586534],"study_design_scores_gemma":[0.0005326107,0.005583813,0.6486399,0.03314714,0.004024964,0.003899219,0.006066697,0.1089922,0.01800861,0.0389718,0.1311417,0.0009914719],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364127,0.3210689,0.4317415,0.07894247,0.00653327,0.002834175,0.001795031,0.002536872,0.01813511],"genre_scores_gemma":[0.6011232,0.02494753,0.3606211,0.004855103,0.002607791,0.002636198,0.0009932186,0.0003906056,0.00182525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1794947,"threshold_uncertainty_score":0.94927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05681069152128062,"score_gpt":0.3794884756639081,"score_spread":0.3226777841426275,"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."}}