{"id":"W4402580232","doi":"10.1503/cmaj.240132","title":"Clinical evaluation of a machine learning–based early warning system for patient deterioration","year":2024,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Warning system; Early warning system; Computer science; Early warning score; Machine learning; Medicine; Artificial intelligence; Unit (ring theory); Medical physics; Medical emergency; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01248723,0.0004295247,0.0005877827,0.0005380695,0.0002734452,0.0006603015,0.0005969355,0.0007559547,0.0008617982],"category_scores_gemma":[0.03368076,0.0002383242,0.0007887798,0.0003312682,0.0007209119,0.0006230678,0.0006584407,0.0007431615,0.000128378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016013,"about_ca_system_score_gemma":0.001803093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005530806,"about_ca_topic_score_gemma":0.0006005327,"domain_scores_codex":[0.9917834,0.005637274,0.0008125221,0.0005841646,0.0009310284,0.0002516635],"domain_scores_gemma":[0.9735596,0.01328741,0.008863784,0.001297989,0.001785144,0.001206029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1410672,0.02512273,0.6316099,0.001378668,0.001543191,0.0002184348,0.0005649777,0.01048168,0.004400143,0.0003490871,0.001262759,0.1820012],"study_design_scores_gemma":[0.02337469,0.517626,0.4167748,0.0002903537,0.001172191,0.0004184851,0.0002760018,0.03146886,0.006818196,0.0003634409,0.001300327,0.0001166419],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975696,0.0003162925,0.001127347,0.0001534376,0.0000337203,0.0004705659,0.00007086629,0.00002123338,0.0002367959],"genre_scores_gemma":[0.9968935,0.0001271049,0.002345366,0.00009339616,0.00005140417,0.0003586943,0.00008537467,0.000002238106,0.0000427997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01248723,"threshold_uncertainty_score":0.06603956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06762822221737615,"score_gpt":0.3723692177290471,"score_spread":0.304740995511671,"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."}}