{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003410077,0.001313382,0.0007463408,0.0009142478,0.0003894618,0.001109274,0.001792743,0.001711,0.002801415],"category_scores_gemma":[0.01359928,0.0006448581,0.0007659493,0.0007688202,0.0004305464,0.001856264,0.001539771,0.00318048,0.001965868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000860628,"about_ca_system_score_gemma":0.001692005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006468382,"about_ca_topic_score_gemma":0.007072544,"domain_scores_codex":[0.9987993,0.0004001202,0.0001000527,0.0003957646,0.0002093248,0.00009547765],"domain_scores_gemma":[0.9963271,0.00204172,0.0001656181,0.0003638346,0.0009654729,0.0001362976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004749958,0.0005643505,0.01153175,0.0003166692,0.0002059042,0.0003127113,0.0003090272,0.1803727,0.01386988,0.002981421,0.02016992,0.7688907],"study_design_scores_gemma":[0.00003254842,0.000101517,0.0008950048,0.00003926253,0.00002972381,0.00009068238,0.00005396823,0.9850948,0.007290699,0.003664501,0.002690396,0.00001690752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07980691,0.001292471,0.9045293,0.002655047,0.0001744879,0.0003655459,0.0010306,0.008107524,0.002038089],"genre_scores_gemma":[0.3077673,0.0008560115,0.6819782,0.001180904,0.00009150136,0.0005499829,0.004118473,0.0004059463,0.003051659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006468382,"threshold_uncertainty_score":0.0180344,"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."}}