{"id":"W4391452166","doi":"10.2196/49138","title":"A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Medicine; Similarity (geometry); Computer science; Surgery; Artificial intelligence","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":[0.00129988,0.00019193,0.0003026256,0.0001555632,0.0001302014,0.0003316958,0.0002629873,0.0001165415,0.0000511939],"category_scores_gemma":[0.0003220974,0.0001492967,0.00004974322,0.0004491183,0.00003409802,0.0004668966,0.0006097779,0.0004525546,0.00009560641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008333899,"about_ca_system_score_gemma":0.0003977249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002013541,"about_ca_topic_score_gemma":0.00003256116,"domain_scores_codex":[0.9971558,0.00014061,0.0008719509,0.0002143728,0.001222083,0.0003952162],"domain_scores_gemma":[0.9984589,0.0005436491,0.00007029861,0.0003448447,0.00007962882,0.0005026592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001563752,0.0001511732,0.7647211,0.0004552538,0.00008629647,0.00009516317,0.06003217,0.0001085454,4.601373e-8,0.0003258861,0.01286017,0.1611486],"study_design_scores_gemma":[0.0003691599,0.0004830339,0.676047,0.000548395,0.00002024101,0.00008916911,0.001221563,0.198083,0.0000094947,0.0001588137,0.1223515,0.0006186155],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971772,0.0000752761,0.02269395,0.003136969,0.0009278137,0.0009060338,0.000003780418,0.000364076,0.000120068],"genre_scores_gemma":[0.9768786,0.000002193247,0.01945883,0.003228256,0.00008896091,0.0002938999,0.00001065934,0.00001090247,0.00002770567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1979744,"threshold_uncertainty_score":0.6088141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0206367563536822,"score_gpt":0.3106204757860002,"score_spread":0.289983719432318,"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."}}