{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02808027,0.002195559,0.005928554,0.02527012,0.001743619,0.006794372,0.002515649,0.004787227,0.006283458],"category_scores_gemma":[0.09022705,0.001566261,0.005522517,0.02130955,0.002249955,0.006416114,0.003297531,0.002708439,0.001160906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005087323,"about_ca_system_score_gemma":0.02618699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004891084,"about_ca_topic_score_gemma":0.008951726,"domain_scores_codex":[0.9791374,0.007987508,0.007309426,0.001069013,0.004021077,0.0004756223],"domain_scores_gemma":[0.9221543,0.06118595,0.007328826,0.001190307,0.007566222,0.0005743075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001191869,0.00005787671,0.0004614561,0.7944394,0.001207215,0.0001767139,0.0007315165,0.0002427974,0.0002631743,0.002079966,0.007364148,0.1928565],"study_design_scores_gemma":[0.00002524612,0.00006364966,0.0005635261,0.9617546,0.002665717,0.0001811606,0.0002965583,0.00007582562,0.00009923008,0.0006959824,0.03356177,0.00001668377],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002557287,0.9972224,0.0003255959,0.0006689242,0.0003316066,0.0003961529,0.00008613399,0.000007611624,0.0007058795],"genre_scores_gemma":[0.002503056,0.9947114,0.0009914943,0.0005006602,0.0002032747,0.000828598,0.0001241004,0.000005947341,0.0001314806],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02808027,"threshold_uncertainty_score":0.1485044,"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."}}