{"id":"W2973387681","doi":"10.1002/lary.28292","title":"Otoscopic diagnosis using computer vision: An automated machine learning approach","year":2019,"lang":"en","type":"article","venue":"The Laryngoscope","topic":"Ear Surgery and Otitis Media","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Medical diagnosis; Artificial intelligence; Computer science; Machine learning; Preprocessor; Triage; Upload; Otorhinolaryngology; Referral; Medicine; Family medicine; Medical emergency; Pathology; World Wide Web; Surgery","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":[0.0004560936,0.00017967,0.0003641833,0.00007865947,0.0001567497,0.00004968757,0.0001550245,0.00009884243,0.0008067278],"category_scores_gemma":[0.00003052211,0.0001176599,0.00008036069,0.000271354,0.00006451975,0.0001797936,0.0001002576,0.0004145481,0.0004120349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003097752,"about_ca_system_score_gemma":0.00005248532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001350136,"about_ca_topic_score_gemma":0.000005734134,"domain_scores_codex":[0.9986457,0.000239383,0.0002223065,0.0003018735,0.0002774597,0.0003133109],"domain_scores_gemma":[0.9991685,0.0001308625,0.00008040339,0.0004282772,0.00004493225,0.0001470785],"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":[0.0001706923,0.0005261132,0.9799862,0.0002733664,0.0001428366,0.0001157518,0.001101825,0.002384181,0.006388213,0.0001364775,0.002098829,0.006675552],"study_design_scores_gemma":[0.001494915,0.0009800248,0.2061397,0.0004420157,0.0001379917,0.0002587784,0.0000769788,0.7777362,0.001663708,0.00001314706,0.01074982,0.0003066533],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947104,0.0005211804,0.0002761165,0.0003055726,0.000579863,0.0005048253,0.000003280916,0.0004609307,0.002637808],"genre_scores_gemma":[0.9924911,0.00007303118,0.004633255,0.001267495,0.0003725045,0.00001514285,0.00009020371,0.00004765862,0.001009564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7753521,"threshold_uncertainty_score":0.8833104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141184903193415,"score_gpt":0.2971068505075384,"score_spread":0.2756950014756042,"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."}}