{"id":"W4362585774","doi":"10.1101/2023.03.30.23287941","title":"Impact of Case and Control Selection on Training AI Screening of Cardiac Amyloidosis","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Amyloidosis: Diagnosis, Treatment, Outcomes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Cedars-Sinai Medical Center","keywords":"Generalizability theory; Cohort; Medicine; Population; Amyloidosis; Feature selection; Internal medicine; Machine learning; Statistics; Computer science; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04522426,0.001239467,0.001103712,0.0009192497,0.0006379283,0.001837428,0.001146941,0.001398235,0.002037152],"category_scores_gemma":[0.09287015,0.0004194667,0.001304494,0.0004701911,0.001175098,0.001280681,0.001185212,0.001977514,0.0004716532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007885954,"about_ca_system_score_gemma":0.001412709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004967065,"about_ca_topic_score_gemma":0.002956729,"domain_scores_codex":[0.9859525,0.01059041,0.000573375,0.001947384,0.0006368349,0.0002996051],"domain_scores_gemma":[0.931925,0.05657199,0.002074021,0.005180711,0.003307985,0.0009404157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009719619,0.001411449,0.5619014,0.0002934568,0.003688593,0.0006505013,0.0003703071,0.2640533,0.003683897,0.001996863,0.007656957,0.1445737],"study_design_scores_gemma":[0.000917269,0.001539491,0.06148186,0.0002367717,0.00169963,0.0005529553,0.0001826485,0.9157103,0.009561868,0.006163391,0.001861079,0.00009271336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9062157,0.002052222,0.08299647,0.002625629,0.0004094479,0.0003395773,0.001147408,0.001166232,0.003047334],"genre_scores_gemma":[0.9905626,0.00008034513,0.007599473,0.0003229012,0.00004914623,0.00006794661,0.0009349585,0.0000526428,0.0003300064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04522426,"threshold_uncertainty_score":0.2391716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03380902677950182,"score_gpt":0.3229430696261395,"score_spread":0.2891340428466377,"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."}}