{"id":"W4388001764","doi":"10.1038/s41746-023-00945-1","title":"Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction","year":2023,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Center for Advancing Translational Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases","keywords":"Ejection fraction; Medicine; Intraclass correlation; Internal medicine; Cardiology; Predictive value; Predictive value of tests; Ultrasound; Nuclear medicine; Radiology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0002610478,0.0001701082,0.0006004272,0.0002669868,0.00004112217,0.000008944888,0.000118999,0.0001160056,0.0002304479],"category_scores_gemma":[0.008483683,0.000123607,0.0001176834,0.001084127,0.0004291338,0.0001888318,0.00002699108,0.0002500195,0.00001967006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009819708,"about_ca_system_score_gemma":0.0001478793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004226716,"about_ca_topic_score_gemma":0.000007655722,"domain_scores_codex":[0.9977789,0.0000299301,0.000994796,0.0002949096,0.0007107867,0.0001906884],"domain_scores_gemma":[0.9919574,0.006319817,0.0005699592,0.0004266265,0.0005997331,0.0001264698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007447598,0.002577805,0.8290899,0.00201847,0.0006615845,0.00006827605,0.0009711394,0.0003111102,0.05446576,0.005109154,0.000881879,0.1031001],"study_design_scores_gemma":[0.001133828,0.00597951,0.9510968,0.002239277,0.0007189711,0.000150566,0.009402617,0.0002518501,0.02336023,0.004372458,0.00105157,0.0002423391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765339,0.00010421,0.01646093,0.0008554033,0.0002101973,0.0008913562,0.00007322191,0.00009338577,0.004777321],"genre_scores_gemma":[0.9982582,0.000144185,0.000700529,0.00003559437,0.0001880673,0.00003638798,0.0005691511,0.00002479626,0.00004313553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1220068,"threshold_uncertainty_score":0.9998683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04687346736303867,"score_gpt":0.376293923046781,"score_spread":0.3294204556837423,"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."}}