{"id":"W7106271742","doi":"10.1016/j.resplu.2025.101175","title":"Artificial Intelligence in cardiopulmonary resuscitation training – A scoping review","year":2025,"lang":"en","type":"article","venue":"Resuscitation Plus","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Cardiopulmonary resuscitation; Training (meteorology); Applications of artificial intelligence; Basic life support; Dialog box","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":[],"consensus_categories":[],"category_scores_codex":[0.001195028,0.0001876928,0.0005331097,0.0006061445,0.0001280088,0.00003938654,0.0000900103,0.000123267,0.00001834859],"category_scores_gemma":[0.001469817,0.0001876363,0.0001964017,0.001671863,0.00009137743,0.0002252079,0.00003433167,0.0002687345,0.00003772718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003285078,"about_ca_system_score_gemma":0.0008065753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000427403,"about_ca_topic_score_gemma":0.0001102562,"domain_scores_codex":[0.9977407,0.0001980465,0.0008639671,0.0004425257,0.0004365701,0.0003182219],"domain_scores_gemma":[0.9988303,0.0003824004,0.0001499727,0.0003010782,0.000243082,0.00009319927],"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":[0.002841564,0.0003480217,0.01007632,0.06670909,0.0003456374,0.0005767986,0.01124438,0.007675369,0.006340153,0.05147542,0.004181339,0.8381859],"study_design_scores_gemma":[0.002887229,0.0002628338,0.2025673,0.6991411,0.001005374,0.00004559979,0.01359123,0.02720792,0.001939376,0.0483179,0.001380198,0.001653992],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3846149,0.1422924,0.1657444,0.0628534,0.01970216,0.02262423,0.00006047769,0.001344973,0.200763],"genre_scores_gemma":[0.9906263,0.004597202,0.002591764,0.00132434,0.0002637912,0.0002297697,0.0001650025,0.00002407787,0.0001777244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8365319,"threshold_uncertainty_score":0.7651587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05000354230916241,"score_gpt":0.362212599289411,"score_spread":0.3122090569802485,"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."}}