{"id":"W2183095575","doi":"10.1017/s148180350001438x","title":"What happens to my patients? An automated linkage between emergency department and mortality data","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen Elizabeth II Health Sciences Centre; Dalhousie University","funders":"","keywords":"Medicine; Emergency department; Linkage (software); Medical emergency; Emergency medicine; Data science; Nursing; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001534968,0.000234326,0.0004174071,0.0003941384,0.0002508716,0.00003890151,0.001874254,0.00008800753,0.0009605061],"category_scores_gemma":[0.0006828141,0.000200076,0.000041297,0.000581576,0.00004575655,0.001731519,0.0001627928,0.0004028926,0.00002074755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001461303,"about_ca_system_score_gemma":0.0005233772,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007489992,"about_ca_topic_score_gemma":0.0694899,"domain_scores_codex":[0.9969201,0.0002502667,0.001236208,0.0004691328,0.0005651803,0.0005591313],"domain_scores_gemma":[0.9952198,0.00003607019,0.0004444198,0.001211247,0.0004913707,0.002597102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003174341,0.00003187752,0.8875414,0.00005322816,0.00007402808,0.00004663528,0.003827194,0.0001607221,0.000009174443,0.0002808756,0.07075781,0.0372139],"study_design_scores_gemma":[0.0004063819,0.0008208377,0.8450091,0.0001786145,0.00006025868,0.000009328331,0.0001583371,0.01169474,0.000004492861,0.0001874743,0.1412101,0.0002603044],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959163,0.002856065,0.003971842,0.02271697,0.01044698,0.0003740262,0.00006316129,0.00007610841,0.000331827],"genre_scores_gemma":[0.9934319,0.0006202437,0.003232122,0.0005144099,0.001911193,0.000002838322,0.0001101654,0.00002339399,0.0001537223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07045229,"threshold_uncertainty_score":0.9999527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1361795584298157,"score_gpt":0.4176734206088111,"score_spread":0.2814938621789955,"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."}}