{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01191961,0.000628352,0.001032352,0.002349092,0.002103087,0.008159772,0.002326096,0.006668635,0.006737805],"category_scores_gemma":[0.02624093,0.0003941222,0.0006472968,0.002829618,0.0115271,0.009592785,0.003385543,0.008929414,0.001280914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005557217,"about_ca_system_score_gemma":0.005269793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005486748,"about_ca_topic_score_gemma":0.003182871,"domain_scores_codex":[0.9904093,0.00658674,0.0004036801,0.0006040088,0.001483632,0.0005127298],"domain_scores_gemma":[0.9716069,0.02250849,0.0009503444,0.0009329818,0.002489203,0.001512142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001075522,0.000151882,0.004654437,0.002062902,0.0001093903,0.00053032,0.002171862,0.005381383,0.0001295588,0.5815238,0.1734645,0.2297124],"study_design_scores_gemma":[0.0000445344,0.00004909729,0.002015408,0.002925815,0.00002325394,0.0003568277,0.001878164,0.003821641,0.0001219337,0.6862206,0.3024744,0.00006823969],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.004022518,0.2472513,0.01396929,0.6348287,0.00543351,0.0001107531,0.0001480511,0.0001535041,0.09408233],"genre_scores_gemma":[0.3848488,0.4050151,0.04207515,0.1284701,0.01855501,0.000633478,0.0005166389,0.0001632246,0.0197225],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01191961,"threshold_uncertainty_score":0.06303763,"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."}}