{"id":"W2052808156","doi":"10.1197/j.aem.2006.02.013","title":"Coded Chief Complaints—Automated Analysis of Free‐text Complaints","year":2006,"lang":"en","type":"article","venue":"Academic Emergency Medicine","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Emergency department; Parsing; Medicine; Text messaging; Complaint; Schema (genetic algorithms); Artificial intelligence; Natural language processing; Machine learning; Computer science; World Wide Web; Nursing","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":[0.00319751,0.00079761,0.0005425765,0.006211933,0.0004580537,0.002028648,0.001450283,0.0007198447,0.002183224],"category_scores_gemma":[0.01430244,0.0002760637,0.0007435799,0.002915937,0.0004563157,0.00176625,0.001008289,0.0005486374,0.001487552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006971771,"about_ca_system_score_gemma":0.001554152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004269663,"about_ca_topic_score_gemma":0.004085399,"domain_scores_codex":[0.9966726,0.00100925,0.0005276675,0.0007582945,0.0008424748,0.0001896178],"domain_scores_gemma":[0.9855598,0.005833012,0.001933843,0.001238207,0.005110925,0.0003241928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001368953,0.0004597206,0.2798045,0.001529459,0.0001923163,0.001145973,0.003391062,0.005511912,0.03624639,0.003224129,0.03188832,0.6352373],"study_design_scores_gemma":[0.0003197316,0.0009620205,0.5737893,0.0008172656,0.0002756981,0.006130013,0.004201861,0.2615871,0.06871714,0.01059484,0.07222793,0.0003771162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5460505,0.001231082,0.3993675,0.001097013,0.0003128531,0.003573145,0.02660981,0.01371552,0.008042559],"genre_scores_gemma":[0.4516899,0.0004014202,0.5137403,0.0005012719,0.0002473174,0.001309746,0.02776082,0.0005635995,0.003785638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006211933,"threshold_uncertainty_score":0.01691025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02943132884608256,"score_gpt":0.3409265199072352,"score_spread":0.3114951910611526,"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."}}