{"id":"W3163682701","doi":"10.2196/25304","title":"Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China; China Postdoctoral Science Foundation","keywords":"Interpretability; Computer science; Medical knowledge; Artificial intelligence; Medical diagnosis; Benchmark (surveying); Process (computing); Medical record; Machine learning; Data science; Data mining; Medicine","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":[0.001228612,0.0001678569,0.000248981,0.0001444134,0.0002185417,0.0001812767,0.000657414,0.0002370213,0.00003845102],"category_scores_gemma":[0.002754589,0.0001505495,0.00004835536,0.0003475758,0.00005299967,0.0004071345,0.0007872786,0.000538472,0.00002014085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001210442,"about_ca_system_score_gemma":0.001548661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003817644,"about_ca_topic_score_gemma":0.000008156066,"domain_scores_codex":[0.9972661,0.00007662585,0.0009316938,0.0002069058,0.001135151,0.0003834888],"domain_scores_gemma":[0.9977302,0.000898469,0.0002074439,0.0002999852,0.0002523093,0.0006115696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002607724,0.0001111018,0.001777913,0.001036725,0.00001231903,0.00001487211,0.02039261,0.0001625308,0.00000134123,0.01573564,0.0003171851,0.9604352],"study_design_scores_gemma":[0.0004861497,0.00006142976,0.0002067941,0.0004002578,0.000004556767,0.0000633777,0.0004224973,0.9922895,0.0006892099,0.0006144565,0.004581887,0.0001798941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07789885,0.0006271497,0.9191736,0.001014896,0.0004762541,0.0003007489,0.000001083101,0.0001369386,0.0003704948],"genre_scores_gemma":[0.5457656,0.0001555267,0.451156,0.002104225,0.0001485602,0.000502763,0.0000298207,0.00001988678,0.0001175901],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9921269,"threshold_uncertainty_score":0.6139231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04218233896602738,"score_gpt":0.3439405876318005,"score_spread":0.3017582486657731,"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."}}