{"id":"W2962968929","doi":"","title":"MIMIC-Extract","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pipeline (software); Machine learning; Benchmark (surveying); Artificial intelligence; Raw data; Outlier; Pipeline transport; USable; Data modeling; Data mining; 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":[0.002246831,0.002352748,0.001320544,0.002444849,0.0006632781,0.003060801,0.0029318,0.00146004,0.02526686],"category_scores_gemma":[0.01663321,0.001097975,0.002601873,0.002173994,0.0008604648,0.002735014,0.005699662,0.001865448,0.02848057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009963248,"about_ca_system_score_gemma":0.003637637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002937021,"about_ca_topic_score_gemma":0.005476778,"domain_scores_codex":[0.9969667,0.0004731004,0.0003037525,0.001036327,0.001008657,0.0002115221],"domain_scores_gemma":[0.9958041,0.001187188,0.0002936101,0.001935284,0.0006147436,0.0001650884],"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":[0.001835692,0.0003327863,0.01425246,0.00179584,0.0007791474,0.000737592,0.0003620383,0.02686122,0.0119049,0.02320937,0.5973827,0.3205463],"study_design_scores_gemma":[0.0004632609,0.0005361455,0.009319203,0.0002329533,0.0002111411,0.00150356,0.0002163059,0.3170102,0.05518765,0.08418924,0.5308454,0.0002849618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01389099,0.001210142,0.4691136,0.001132373,0.0007362686,0.001195558,0.1101665,0.3874402,0.01511433],"genre_scores_gemma":[0.1101737,0.001069742,0.4636562,0.00176566,0.000413736,0.002512036,0.3758892,0.02561558,0.01890421],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02526686,"threshold_uncertainty_score":0.08452606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1196011531810332,"score_gpt":0.2193688871490302,"score_spread":0.09976773396799696,"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."}}