{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007001665,0.0004740374,0.001725219,0.0009540514,0.000202626,3.657634e-7,0.000501865,0.000331077,0.005616549],"category_scores_gemma":[0.0007427252,0.0003733293,0.0004457913,0.003576524,0.0005562507,0.00009451249,0.0001714414,0.0007083566,0.00004799517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007952676,"about_ca_system_score_gemma":0.00004570298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001540576,"about_ca_topic_score_gemma":0.0002836869,"domain_scores_codex":[0.9953353,0.0001155379,0.002307987,0.0006169298,0.0009958075,0.0006284292],"domain_scores_gemma":[0.9975085,0.0001646882,0.0006771719,0.0008422259,0.0005792677,0.0002281294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001782711,0.000213589,0.3337353,0.0002721748,0.00366321,0.0000289843,0.0008749696,0.0001454906,0.03205417,0.003478175,0.6251769,0.0001788266],"study_design_scores_gemma":[0.005528676,0.0008531658,0.9096622,0.0008119282,0.0167774,0.00004990764,0.001757164,0.01513779,0.003352599,0.001855572,0.04327639,0.0009372279],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9087499,0.02256336,0.0004768512,0.004494505,0.001941592,0.0006997632,0.0002271008,0.0006711786,0.0601757],"genre_scores_gemma":[0.9855133,0.01021687,0.0002055963,0.0004098677,0.0008197271,0.00003091997,0.0008030376,0.00004868285,0.001951957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5819005,"threshold_uncertainty_score":0.9998719,"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."}}