{"id":"W3191321518","doi":"10.1016/j.ultrasmedbio.2021.07.001","title":"Strain Estimation of the Murine Right Ventricle Using High-Frequency Speckle-Tracking Ultrasound","year":2021,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Pulmonary Hypertension Research and Treatments","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"FujiFilm VisualSonics (Canada)","funders":"National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Speckle pattern; Ultrasound; Strain (injury); Ventricle; Speckle tracking echocardiography; Tracking (education); Biomedical engineering; Internal medicine; Cardiology; Medicine; Radiology; Computer science; Ejection fraction; Artificial intelligence; Heart failure","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":[0.0007666609,0.00028678,0.000294712,0.0007856832,0.0001725441,0.0003679181,0.0003023464,0.0007125505,0.0004740059],"category_scores_gemma":[0.0006071195,0.0002625953,0.0002387831,0.0002597789,0.0002963518,0.0003714208,0.0002566962,0.0004450347,0.0001472971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001500431,"about_ca_system_score_gemma":0.0001637919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005828722,"about_ca_topic_score_gemma":0.0009439717,"domain_scores_codex":[0.9997395,0.00004352169,0.00001882184,0.00006668171,0.0001097509,0.00002168588],"domain_scores_gemma":[0.9994174,0.0001651596,0.0001793167,0.00007212509,0.0001181198,0.00004796331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007680357,0.00004448251,0.001809939,0.00002996102,0.000008646641,0.00002370514,0.00003823811,0.001660629,0.9905294,0.00009552718,0.00002223073,0.005660532],"study_design_scores_gemma":[0.00002579328,0.0007104519,0.05226655,0.00003872532,0.00007332995,0.0002521295,0.0000857157,0.07016096,0.8753661,0.0002703709,0.0007043358,0.00004553429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8937454,0.0006351994,0.1041142,0.00006694672,0.0000340763,0.00003294835,0.0002285854,0.0001922994,0.0009503218],"genre_scores_gemma":[0.9452201,0.0005888376,0.05233774,0.00005314565,0.00001881781,0.00006005777,0.0002119494,0.0000573935,0.001451956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007856832,"threshold_uncertainty_score":0.004054546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163522751423174,"score_gpt":0.3333837034947522,"score_spread":0.3017484759805204,"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."}}