{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004121809,0.0004156503,0.0007468926,0.004384076,0.0009047644,0.002322245,0.001218579,0.001482696,0.002234532],"category_scores_gemma":[0.02350519,0.0004147623,0.0005358446,0.003936679,0.0002213642,0.001647667,0.002010499,0.001197413,0.001401913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009155881,"about_ca_system_score_gemma":0.002413651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009710228,"about_ca_topic_score_gemma":0.01546832,"domain_scores_codex":[0.9970858,0.0008688573,0.0004459955,0.0006959923,0.0006856886,0.0002177598],"domain_scores_gemma":[0.9850391,0.007875946,0.002295698,0.001674177,0.002444755,0.0006702296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001358765,0.0007189783,0.6970409,0.0004021373,0.0005089757,0.001525795,0.00106859,0.005116361,0.003395025,0.001291465,0.06466499,0.222908],"study_design_scores_gemma":[0.0007602703,0.00094242,0.6176886,0.0006735975,0.001246547,0.005266261,0.006590729,0.232279,0.01581383,0.01535285,0.1029889,0.0003970531],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8494918,0.002243411,0.04955611,0.01683979,0.0008723222,0.0009386293,0.06733251,0.004917913,0.007807579],"genre_scores_gemma":[0.8503835,0.0007935654,0.0983946,0.001683739,0.0006349364,0.0004564618,0.04513169,0.0001526065,0.002368934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009710228,"threshold_uncertainty_score":0.02179849,"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."}}