{"id":"W3159875333","doi":"10.1016/j.medj.2021.04.006","title":"Machine learning in clinical decision making","year":2021,"lang":"no","type":"review","venue":"Med","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":273,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Horizon 2020; Horizon 2020 Framework Programme; Canadian Institute for Advanced Research; Howard Hughes Medical Institute; Weizmann Institute of Science; Bill and Melinda Gates Foundation","keywords":"Clinical decision making; Computer science; Artificial intelligence; Psychology; Machine learning; Medicine; Intensive care medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009927634,0.0008349704,0.003615258,0.0006200099,0.00033149,0.0004489617,0.002657224,0.00106241,0.0008124273],"category_scores_gemma":[0.01624436,0.0007737472,0.001018369,0.002389036,0.0001017662,0.0002111096,0.002457048,0.007813272,0.0008903402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004280534,"about_ca_system_score_gemma":0.001570164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000264007,"about_ca_topic_score_gemma":0.0003204484,"domain_scores_codex":[0.9832994,0.008304624,0.003724087,0.002285631,0.001246874,0.00113935],"domain_scores_gemma":[0.9873289,0.008661667,0.001485071,0.001909837,0.0002588372,0.0003557188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008684973,0.00009077886,0.02864844,0.003418642,0.00003419945,0.000893753,0.0002639536,0.0003061479,1.700154e-8,0.000687739,0.0001775529,0.9654701],"study_design_scores_gemma":[0.0004301444,0.0001695618,0.005010725,0.04680426,0.00006549218,0.0001700435,0.00001069742,0.09834961,3.178262e-8,0.0001200943,0.8482229,0.0006464356],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000167737,0.9663128,0.02570273,0.0008266781,0.004661259,0.0008184998,0.000005728185,0.0002254655,0.001279067],"genre_scores_gemma":[0.002426745,0.956273,0.03809562,0.0005736212,0.0008940122,0.00005939059,0.0000804166,0.0001209944,0.001476158],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9648237,"threshold_uncertainty_score":0.9998876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1158095976810062,"score_gpt":0.4713244752326469,"score_spread":0.3555148775516406,"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."}}