{"id":"W4380151099","doi":"10.1101/2023.06.06.23290887","title":"GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"AI in cancer detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Health Research Foundation","funders":"National Human Genome Research Institute; National Institutes of Health","keywords":"Benchmarking; Interoperability; Medicine; Computer science; Database; World Wide Web","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":[0.001928402,0.001150085,0.0009595398,0.005681778,0.0006056242,0.001744015,0.002268454,0.001463313,0.0141557],"category_scores_gemma":[0.007509578,0.0004283622,0.001015043,0.002947822,0.0004197191,0.001354173,0.003667261,0.00106645,0.01263957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007167827,"about_ca_system_score_gemma":0.001837827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004087749,"about_ca_topic_score_gemma":0.00601494,"domain_scores_codex":[0.998483,0.0001998064,0.0003200088,0.0004751449,0.0004060144,0.0001160364],"domain_scores_gemma":[0.9973539,0.0006304603,0.0003782055,0.0009663624,0.0004355643,0.000235614],"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":[0.001167012,0.0001884393,0.02826476,0.004584956,0.0004020949,0.002143788,0.0006439636,0.003407074,0.02177362,0.00705695,0.7562834,0.1740839],"study_design_scores_gemma":[0.0002493855,0.0001531417,0.06601772,0.001196798,0.0003040293,0.00716367,0.0004361511,0.007144985,0.01412988,0.00792785,0.8951114,0.0001650563],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02581176,0.005015411,0.02817387,0.0007024578,0.0003087048,0.0003995916,0.9112489,0.01891753,0.009421751],"genre_scores_gemma":[0.02510558,0.001213651,0.02344416,0.0003208352,0.00004568629,0.0005453282,0.9464806,0.001135942,0.001708305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0141557,"threshold_uncertainty_score":0.04735547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07835171933478706,"score_gpt":0.3369114019507723,"score_spread":0.2585596826159852,"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."}}