{"id":"W4385783269","doi":"10.51731/cjht.2023.712","title":"Artificial Intelligence in Prehospital Emergency Health Care","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Health Technologies","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Triage; Health care; Staffing; Artificial intelligence; Conversation; Computer science; Medical emergency; Medicine; Nursing; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001075114,0.0001338537,0.0004322653,0.001608484,0.0002626292,0.00001610361,0.0002417064,0.0001677647,0.00005784848],"category_scores_gemma":[0.001117714,0.0001255088,0.0000869489,0.001617,0.0001341166,0.0001155528,0.00001636306,0.0006428134,0.00007451268],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111526,"about_ca_system_score_gemma":0.008349245,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02087509,"about_ca_topic_score_gemma":0.03784962,"domain_scores_codex":[0.9973004,0.00007080525,0.001465564,0.0001906459,0.0002076239,0.0007649035],"domain_scores_gemma":[0.9987406,0.00005214693,0.0004295609,0.0002580548,0.0003025304,0.0002170981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001419999,0.00001861972,0.02228014,0.0002476395,0.000006792152,0.00007317982,0.007566541,0.00004535896,0.000004157262,0.001098069,0.003193681,0.9654516],"study_design_scores_gemma":[0.00009289091,0.006464279,0.1076093,0.00259949,0.00002241061,0.0004864584,0.715822,0.0004019081,0.004113728,0.08807054,0.07362876,0.0006882439],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7007836,0.0664466,0.0005539557,0.228094,0.002912622,0.0008068126,0.00001730989,0.0002287372,0.0001563659],"genre_scores_gemma":[0.996152,0.002894316,0.0004127139,0.0004104041,0.00006020234,0.00001355817,0.00001221101,0.00001740692,0.00002713979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9647634,"threshold_uncertainty_score":0.9972725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1646516406727279,"score_gpt":0.4372382342957434,"score_spread":0.2725865936230156,"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."}}