{"id":"W3155967961","doi":"10.1186/s42234-021-00066-8","title":"How artificial intelligence can help us ‘Choose Wisely’","year":2021,"lang":"en","type":"article","venue":"Bioelectronic Medicine","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women's College Hospital; Trillium Health Centre; University of Toronto; Canada Research Chairs; Toronto Public Health; University of King's College","funders":"","keywords":"Workflow; Leverage (statistics); Computer science; Clinical decision support system; Artificial intelligence; Knowledge management; Decision support system; Risk analysis (engineering); Medicine","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":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003409307,0.0003386207,0.0006721651,0.0001768863,0.000788447,0.00002592689,0.0003935923,0.0003952958,0.002647328],"category_scores_gemma":[0.008463562,0.000284422,0.00007957972,0.0009738196,0.0003010045,0.0001964645,0.0001623214,0.002162442,0.0004213039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008963954,"about_ca_system_score_gemma":0.004513373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002341083,"about_ca_topic_score_gemma":0.01150305,"domain_scores_codex":[0.992971,0.00280145,0.001051234,0.0007621931,0.000811561,0.00160254],"domain_scores_gemma":[0.995297,0.001759181,0.000549566,0.0009483314,0.0008187497,0.0006272037],"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":[0.0005733321,0.0007319244,0.02210263,0.002872983,0.0004089282,0.000804089,0.01656703,0.000003789854,0.04371641,0.3339722,0.07696209,0.5012846],"study_design_scores_gemma":[0.001204614,0.001768988,0.006165456,0.001723645,0.0002618547,0.0001129171,0.03609591,0.0003429352,0.0137518,0.03611314,0.9014378,0.001020923],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2507364,0.0162966,0.004766743,0.7175665,0.003913282,0.001377406,0.00003471174,0.0003914407,0.004916804],"genre_scores_gemma":[0.96088,0.002865675,0.0003226795,0.0235748,0.003811404,0.0001455394,0.0001466102,0.00006517729,0.008188115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8244757,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.609484213104878,"score_gpt":0.5433396321805208,"score_spread":0.06614458092435715,"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."}}