{"id":"W2608077224","doi":"10.1503/cmaj.1095413","title":"What “learning” machines will mean for medicine","year":2017,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Artificial intelligence; Computer science; Data science; Operations research; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007175139,0.0007444413,0.0006500184,0.0008734177,0.002223178,0.005664287,0.001414177,0.005539692,0.01210539],"category_scores_gemma":[0.01938479,0.000399547,0.0006382369,0.0006019966,0.01419572,0.01552913,0.001775113,0.005794225,0.005276316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002514961,"about_ca_system_score_gemma":0.003104442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003133544,"about_ca_topic_score_gemma":0.002092918,"domain_scores_codex":[0.9963962,0.001893012,0.0001049992,0.0004934153,0.0007888958,0.0003233574],"domain_scores_gemma":[0.9932982,0.003651285,0.0003293499,0.0009048382,0.001136707,0.0006796005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008511525,0.00004905671,0.001893519,0.0004212348,0.00008274647,0.00007825168,0.0006018875,0.001687074,0.0002162825,0.788868,0.1344897,0.07152707],"study_design_scores_gemma":[0.0000205803,0.00003641874,0.0008700374,0.0004902584,0.00002881297,0.000129134,0.0002920026,0.001411097,0.0003783132,0.8011242,0.1951793,0.00003991669],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.004339403,0.05544809,0.03171044,0.8051066,0.01136258,0.00004561367,0.0005795365,0.0004350841,0.09097262],"genre_scores_gemma":[0.5149985,0.1057319,0.05712418,0.2328151,0.044357,0.0004807407,0.0008712456,0.0005702676,0.0430511],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9977768,"threshold_uncertainty_score":0.04049659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07968710049239681,"score_gpt":0.414649056148207,"score_spread":0.3349619556558102,"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."}}