{"id":"W4200375536","doi":"10.46747/cfp.6712889","title":"Primer for artificial intelligence in primary care","year":2021,"lang":"en","type":"article","venue":"Canadian Family Physician","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"College of Family Physicians of Canada; Western University","funders":"","keywords":"Primary care; Computer science; Artificial intelligence; Stakeholder; Data science; Knowledge management; Medicine; Management; Family medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004669763,0.0006730571,0.0008094168,0.001698076,0.001032465,0.004313797,0.002048979,0.00643978,0.04758891],"category_scores_gemma":[0.01213245,0.0004934773,0.0006583874,0.001707368,0.003486861,0.004419958,0.00229239,0.01240661,0.01745337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001780495,"about_ca_system_score_gemma":0.003665124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001746831,"about_ca_topic_score_gemma":0.002754481,"domain_scores_codex":[0.9973946,0.001278049,0.0002086116,0.000236809,0.0007637481,0.0001181073],"domain_scores_gemma":[0.9860642,0.01123652,0.0003570652,0.0005969831,0.001085362,0.0006598684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003007069,0.00005101089,0.0002738435,0.0007576657,0.00001625864,0.0002299386,0.0008041731,0.000242247,0.0002694523,0.2169166,0.5750415,0.2053672],"study_design_scores_gemma":[0.000006284136,0.0000107959,0.0001355385,0.0008618271,0.000003704322,0.0002539358,0.00009821985,0.0001403889,0.0000435983,0.04296116,0.9554777,0.000006900662],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0005311132,0.2418597,0.08055916,0.4077136,0.02906728,0.0003077842,0.001021344,0.001542284,0.2373977],"genre_scores_gemma":[0.02703828,0.31736,0.1683406,0.2415196,0.04126893,0.002389256,0.001594798,0.001474714,0.1990139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04758891,"threshold_uncertainty_score":0.1592008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03114812706395331,"score_gpt":0.2777856964996293,"score_spread":0.2466375694356759,"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."}}