{"id":"W3176396634","doi":"10.2139/ssrn.3857668","title":"Achieving Clinical Automation in Emergency Medicine with Machine Learning Medical Directives","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Genome Canada; Fleming College; Hospital for Sick Children; SickKids Foundation; University of Toronto; University Health Network; Canada Research Chairs","funders":"","keywords":"Automation; Medical emergency; Medicine; Artificial intelligence; Computer science; Engineering; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008508365,0.0002014754,0.0003922244,0.0002205034,0.0002964937,0.00003770213,0.0007398518,0.0001471619,0.0002606204],"category_scores_gemma":[0.003488437,0.0001542311,0.00008795122,0.0009526364,0.00006374525,0.0004061702,0.0001857271,0.006859825,0.00001399576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004973973,"about_ca_system_score_gemma":0.003625079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002763784,"about_ca_topic_score_gemma":0.005302321,"domain_scores_codex":[0.9942376,0.00169477,0.0009689377,0.0004947577,0.0009512406,0.001652698],"domain_scores_gemma":[0.9984289,0.0003823086,0.0004089163,0.0003125865,0.0002254223,0.0002418708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002865106,0.0001065597,0.7378893,0.00002769279,0.00007734955,0.0001243608,0.0008493576,0.0005082345,0.0000232189,0.07070924,0.00003006233,0.189626],"study_design_scores_gemma":[0.004368059,0.003385932,0.5741354,0.0009516223,0.00004741238,0.007328671,0.001709819,0.3368278,0.000016782,0.06440723,0.00593679,0.0008844233],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3998951,0.01521429,0.5367206,0.04327101,0.001389315,0.0001756254,2.579603e-7,0.0003242758,0.003009495],"genre_scores_gemma":[0.9835871,0.01215471,0.002388299,0.0001672637,0.0006174808,0.000005871417,0.000005458888,0.00002679281,0.001046996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.583692,"threshold_uncertainty_score":0.9954314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824877534620318,"score_gpt":0.349619883738453,"score_spread":0.3313711083922499,"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."}}