{"id":"W4386631886","doi":"10.1089/derm.2023.0148","title":"Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity","year":2023,"lang":"en","type":"article","venue":"Dermatitis","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Generalizability theory; Convolutional neural network; Medicine; Discriminative model; Patch test; Receiver operating characteristic; Binary classification; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Statistics; Machine learning; Computer science; Mathematics; Internal medicine; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007364334,0.00006726945,0.0001325003,0.0001119657,0.00003399018,0.000008850879,0.00003032094,0.00003725834,0.00008340026],"category_scores_gemma":[0.00007825249,0.00006742852,0.00004462939,0.0002919965,0.00002267843,0.00004485009,0.00002939479,0.0000653487,0.00007566313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003303835,"about_ca_system_score_gemma":0.00001410038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009672176,"about_ca_topic_score_gemma":0.0001520241,"domain_scores_codex":[0.9994001,0.00001698387,0.0001810735,0.0001110801,0.0001615365,0.0001292168],"domain_scores_gemma":[0.9995904,0.00009273351,0.00004961493,0.00013144,0.00005728274,0.00007850581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009374513,0.000453595,0.2716009,0.0003686452,0.0001361808,0.001068929,0.0005011648,0.003482202,0.03182173,0.0006308638,0.2798831,0.409959],"study_design_scores_gemma":[0.0005538643,0.0002775571,0.699139,0.00005160692,0.00004437307,0.0005662496,0.00007598791,0.2766057,0.003154822,0.00003166613,0.01938279,0.0001163667],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828523,0.00001310814,0.006532159,0.00495472,0.0003307753,0.0003959772,0.00002016519,0.0001727262,0.004728061],"genre_scores_gemma":[0.997443,0.00001571134,0.0002359252,0.001131188,0.0001039713,0.00002313694,0.0000556628,0.000008408774,0.0009829998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4275381,"threshold_uncertainty_score":0.2749655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189472797521371,"score_gpt":0.2504671887619427,"score_spread":0.2315199090098056,"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."}}