{"id":"W4312062806","doi":"10.1177/08465371221145023","title":"Machine Learning Model for Chest Radiographs: Using Local Data to Enhance Performance","year":2022,"lang":"en","type":"article","venue":"Canadian Association of Radiologists Journal","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; Vancouver Coastal Health; University of British Columbia","funders":"","keywords":"Generalizability theory; Radiography; Medicine; Wilcoxon signed-rank test; Artificial intelligence; Receiver operating characteristic; Gold standard (test); Computer science; Machine learning; Deep learning; Chest radiograph; Pattern recognition (psychology); Radiology; Statistics","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":[0.002518938,0.0001427358,0.000384507,0.0005818625,0.0006887317,0.00003226987,0.0004243442,0.00009089654,0.00006930764],"category_scores_gemma":[0.001867642,0.0001575318,0.0001173919,0.0004664118,0.00004805015,0.0001450037,0.00008422049,0.0006799524,0.000001633708],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005053212,"about_ca_system_score_gemma":0.002150674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004054403,"about_ca_topic_score_gemma":0.005891037,"domain_scores_codex":[0.9982203,0.0001527683,0.0004414735,0.000302093,0.0003866484,0.0004967049],"domain_scores_gemma":[0.99818,0.000270068,0.0005014926,0.0003164034,0.0002523852,0.0004796338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001699963,0.00006902953,0.1761274,0.00008320787,0.0002428552,0.000032734,0.0007102441,0.7394418,0.0008884057,0.00002571609,0.06343837,0.01877031],"study_design_scores_gemma":[0.0007804605,0.0003557829,0.009802824,0.00005729451,0.0001264738,0.000193374,0.0001005303,0.8753893,0.00009634046,0.00002353704,0.1128969,0.0001771712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7011589,0.001909152,0.22482,0.06733987,0.001470434,0.001485888,0.001578553,0.00009878063,0.0001383742],"genre_scores_gemma":[0.9835979,0.0001723235,0.008864975,0.006342011,0.0002192347,0.00001894862,0.0001931856,0.00003150249,0.0005598962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.282439,"threshold_uncertainty_score":0.9987662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06537947080851796,"score_gpt":0.3375732190702841,"score_spread":0.2721937482617662,"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."}}