{"id":"W4390043358","doi":"10.37964/cr24772","title":"ADVICE: The physician executive’s crash course on AI in health care","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Physician Leadership","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Advice (programming); Framing (construction); Crash; Health care; Psychology; Medical education; Medicine; Computer science; Political science; Engineering","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":[0.0005397788,0.0001656485,0.0003432297,0.0004328183,0.0002643987,0.00004109558,0.0002254038,0.00008593529,0.00001891679],"category_scores_gemma":[0.0001303315,0.0001327178,0.0001531046,0.001007276,0.0001801711,0.000133514,0.000004110518,0.0008555359,0.0002129664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007322512,"about_ca_system_score_gemma":0.005234849,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009503302,"about_ca_topic_score_gemma":0.04406745,"domain_scores_codex":[0.9980897,0.0001758276,0.0005457523,0.000179076,0.0003074975,0.0007022027],"domain_scores_gemma":[0.9983394,0.0001734737,0.0002683776,0.0002723605,0.0003171987,0.0006291599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.000223639,0.0002172607,0.02818041,0.0007940053,0.0001399771,0.0005441381,0.1310924,0.001007522,0.0002123164,0.003943105,0.4336877,0.3999576],"study_design_scores_gemma":[0.0004913115,0.002311202,0.2032751,0.004198259,0.0001023741,0.00005476263,0.69247,0.0003656945,0.0022999,0.003098388,0.09081272,0.0005202743],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5123754,0.003438652,0.0000153572,0.4748617,0.002562363,0.0008752429,0.00002728691,0.00005581265,0.005788194],"genre_scores_gemma":[0.9431931,0.00003475125,0.00001042,0.05467748,0.001681698,0.000006991205,0.00001709546,0.00003087164,0.0003475973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5613776,"threshold_uncertainty_score":0.9970925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.194003030109457,"score_gpt":0.4139969484551544,"score_spread":0.2199939183456974,"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."}}