{"id":"W3134917835","doi":"10.1148/ryai.2021210005","title":"Toward a More Quantitative and Specific Representation of Normality","year":2021,"lang":"en","type":"letter","venue":"Radiology Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Intégré de Santé et Services Sociaux de Chaudière-Appalache","funders":"","keywords":"Generalizability theory; Artificial intelligence; Representation (politics); Medical imaging; Normality; Set (abstract data type); Medical diagnosis; Benchmarking; Computer science; Psychology; Medical education; Medicine; Radiology; Social psychology; Management; Political science; Politics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003505175,0.0002952629,0.0009956183,0.0002719258,0.00006028376,0.00002231857,0.0001649235,0.0007826441,0.0004486239],"category_scores_gemma":[0.001231768,0.0002954926,0.0002033466,0.0004385524,0.001004362,0.00006540054,0.0001001053,0.001249711,0.00005828128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001283313,"about_ca_system_score_gemma":0.0002512279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000272058,"about_ca_topic_score_gemma":0.00001724796,"domain_scores_codex":[0.9973506,0.000381367,0.000825029,0.0008083925,0.00029443,0.0003401326],"domain_scores_gemma":[0.9967596,0.001808654,0.0003706331,0.0005989591,0.0003914726,0.00007070407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005107315,0.0002408547,0.001717253,0.001325109,0.0004850533,0.003525687,0.005649513,0.0001342152,0.007920248,0.004392439,0.9539472,0.02015165],"study_design_scores_gemma":[0.0003704324,0.00199138,0.01167346,0.00215811,0.001244427,0.002283533,0.004414394,0.003362026,0.2246149,0.01614203,0.7300492,0.001696132],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.02203275,0.002912167,0.0151973,0.957939,0.0009415455,0.0006326467,0.00007601879,0.00006388525,0.0002046387],"genre_scores_gemma":[0.08226556,0.003616852,0.009401196,0.8995857,0.003567414,0.00008495527,0.0008748352,0.0001080105,0.0004955351],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.223898,"threshold_uncertainty_score":0.9999497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2049823425090261,"score_gpt":0.4081260180698439,"score_spread":0.2031436755608177,"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."}}