{"id":"W3000871037","doi":"10.1016/j.echo.2019.11.003","title":"Blood Speckle-Tracking Based on High–Frame Rate Ultrasound Imaging in Pediatric Cardiology","year":2020,"lang":"en","type":"article","venue":"Journal of the American Society of Echocardiography","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":113,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; SickKids Foundation; University of Toronto","funders":"St. Olavs Hospital Universitetssykehuset i Trondheim; Norges Forskningsråd; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Medicine; Cardiology; Ultrasound; Internal medicine; Imaging phantom; Speckle tracking echocardiography; Speckle pattern; Blood flow; Frame rate; Cardiac cycle; Biomedical engineering; Nuclear medicine; Radiology; Heart failure; Artificial intelligence; Ejection fraction","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":[0.0009356232,0.0002217411,0.001146223,0.0002760338,0.00006912991,0.00001868767,0.0002525897,0.00006330861,0.000008966388],"category_scores_gemma":[0.0002331697,0.0001520658,0.005036429,0.002166894,0.0003682374,0.00007089656,0.0000352017,0.0008414083,0.000001678546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005538424,"about_ca_system_score_gemma":0.0001461113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005472582,"about_ca_topic_score_gemma":2.882437e-7,"domain_scores_codex":[0.9979075,0.0003562706,0.0005999499,0.0002419779,0.000614536,0.0002798181],"domain_scores_gemma":[0.9980638,0.0003282338,0.0008067183,0.0003807441,0.0002280285,0.0001924764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002757237,0.00007266073,0.9801081,0.00004858763,0.0009530758,0.0000453227,0.000179102,0.007408026,0.004885891,0.000001882906,0.00527772,0.0007438376],"study_design_scores_gemma":[0.002310631,0.0004778799,0.9920111,0.00007046441,0.001372687,0.0001381859,0.0009246578,0.0001436224,0.0009712168,0.00002167334,0.001404545,0.0001533491],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922785,0.0006220538,0.002489126,0.00378638,0.0004300199,0.0001929592,0.00001121682,0.00002291587,0.0001668871],"genre_scores_gemma":[0.9898489,0.002727795,0.001427215,0.004893948,0.001064379,0.000001504454,0.000001866846,0.00003187104,0.000002582645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01190292,"threshold_uncertainty_score":0.6201062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01082132032921189,"score_gpt":0.2375626337860933,"score_spread":0.2267413134568814,"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."}}