{"id":"W4290975873","doi":"10.2196/38178","title":"One Clinician Is All You Need–Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute","keywords":"Computer science; Heuristics; Artificial intelligence; Machine learning; Annotation; Data extraction; Transformer; F1 score; Natural language processing; Magnetic resonance imaging; Algorithm; Data mining; Medicine; MEDLINE; Radiology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002229039,0.0002675486,0.00055407,0.0002070668,0.0004136799,0.0000668732,0.0002173181,0.0001037682,0.001256837],"category_scores_gemma":[0.000667199,0.0002869793,0.00022622,0.0003757026,0.0001252821,0.000171716,0.0003318137,0.001537011,0.0002520745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005868338,"about_ca_system_score_gemma":0.0006815915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000183871,"about_ca_topic_score_gemma":9.768537e-7,"domain_scores_codex":[0.9942403,0.0001188227,0.001184665,0.0002263155,0.003616284,0.0006136211],"domain_scores_gemma":[0.9982571,0.0002117471,0.0002594812,0.0003950517,0.0002859205,0.0005906836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004248197,0.0002949611,0.01387975,0.0001286435,0.00006921692,0.00007738058,0.00849464,0.00001497323,0.000007337907,0.00001345205,0.01861506,0.9583621],"study_design_scores_gemma":[0.001871541,0.0002098813,0.03958841,0.0002709354,0.0001624777,0.0001821931,0.007886721,0.01960681,0.00009704583,0.00001690669,0.929743,0.0003641079],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.2428558,0.2047891,0.1738144,0.08762316,0.03110826,0.01633817,0.0003705779,0.006710776,0.2363897],"genre_scores_gemma":[0.6205508,0.003986924,0.2601503,0.09932921,0.00559937,0.003141096,0.001161536,0.0004788202,0.005602032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.957998,"threshold_uncertainty_score":0.9999582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02634220613993527,"score_gpt":0.304376194747156,"score_spread":0.2780339886072207,"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."}}