{"id":"W2983617361","doi":"10.21037/atm.2019.10.99","title":"Real-life clinical data mining: generating hypotheses for evidence-based medicine","year":2020,"lang":"en","type":"letter","venue":"Annals of Translational Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Clinical trial; Randomized controlled trial; Medicine; Computer science; Data science; Data mining; Intensive care medicine; Medical physics; Internal 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"],"consensus_categories":[],"category_scores_codex":[0.004019133,0.0005052078,0.001517585,0.0003447273,0.0001860926,0.0000279395,0.003639509,0.00054623,0.0001527815],"category_scores_gemma":[0.01790116,0.0003979379,0.0002163486,0.0005373678,0.0005639449,0.0004278347,0.0001638006,0.001511599,0.000005760862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001077247,"about_ca_system_score_gemma":0.001308915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003752826,"about_ca_topic_score_gemma":0.00001645608,"domain_scores_codex":[0.9927058,0.0009409779,0.002429791,0.001472867,0.001914165,0.0005364238],"domain_scores_gemma":[0.9744362,0.02108198,0.001275388,0.001909151,0.0009572509,0.0003400577],"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":[0.00007712028,0.00001884351,0.001460036,0.001408456,0.0001294438,0.00005427942,0.000358828,0.000268911,0.00002309929,0.0006155663,0.9749444,0.02064098],"study_design_scores_gemma":[0.001344623,0.002001407,0.002146509,0.00553757,0.0001606567,0.000008279012,0.00001766136,0.4633293,0.00001125721,0.001529763,0.523436,0.000477071],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.00005338173,0.004596839,0.1962668,0.7969805,0.001041979,0.0006194722,0.0001596695,0.0001273764,0.0001539303],"genre_scores_gemma":[0.003688395,0.001116282,0.1133659,0.8565721,0.0228451,0.00004918499,0.002196731,0.00007112306,0.00009525139],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.4630603,"threshold_uncertainty_score":0.9998472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6699048116500113,"score_gpt":0.510068318658579,"score_spread":0.1598364929914322,"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."}}