{"id":"W4394003149","doi":"10.2196/53787","title":"The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Triage; Coronavirus disease 2019 (COVID-19); Medicine; MEDLINE; Medical emergency; Computer science; World Wide Web; Political science; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001500893,0.00009553129,0.0002771755,0.0001000827,0.00005887383,0.000004767927,0.0001328489,0.0001114202,0.0007291494],"category_scores_gemma":[0.0004312116,0.00005646207,0.00007023976,0.0004746612,0.00008101392,0.0002071559,0.00001994021,0.0003853666,0.00003429463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004545967,"about_ca_system_score_gemma":0.0004728671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001130288,"about_ca_topic_score_gemma":0.0001803961,"domain_scores_codex":[0.9976081,0.00002994333,0.001414304,0.00006795657,0.0006109535,0.0002687809],"domain_scores_gemma":[0.9992329,0.0002318157,0.00009008885,0.0001989836,0.00008284245,0.0001633982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00003086034,0.0001164572,0.0004419602,0.06343048,0.00003650688,0.00001357118,0.118174,0.000003988268,0.0000582291,0.01658535,0.005337068,0.7957715],"study_design_scores_gemma":[0.0002691305,0.0005248051,0.00007358997,0.5708509,0.0001705464,0.0001214969,0.1525218,0.2290574,0.002959831,0.01415907,0.02887977,0.0004115686],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2354432,0.6451958,0.005756601,0.02697948,0.00270004,0.005429393,0.00001186402,0.0002533098,0.07823034],"genre_scores_gemma":[0.8588988,0.1386029,0.0001225393,0.001447636,0.0004324815,0.0002082041,0.00003793296,0.00001932378,0.0002301786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7953599,"threshold_uncertainty_score":0.7983675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08114726538426383,"score_gpt":0.474596551971404,"score_spread":0.3934492865871402,"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."}}