{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07540092,0.001083531,0.002879898,0.00399466,0.001826007,0.006178143,0.00443839,0.01818166,0.003195524],"category_scores_gemma":[0.3562761,0.00139156,0.002243516,0.002979201,0.009337444,0.00947119,0.004049293,0.03265437,0.004605634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004464816,"about_ca_system_score_gemma":0.004342298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201051,"about_ca_topic_score_gemma":0.00322396,"domain_scores_codex":[0.93439,0.05136058,0.004619336,0.002758962,0.006275441,0.0005955389],"domain_scores_gemma":[0.4573789,0.4966777,0.008603478,0.01447834,0.01985102,0.003010615],"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":[0.0005837221,0.0001442463,0.006723164,0.001241684,0.0007776481,0.001386627,0.0008096473,0.001956039,0.0002823619,0.04682029,0.6554487,0.2838258],"study_design_scores_gemma":[0.00077395,0.0003593563,0.00306745,0.003242251,0.00032538,0.004074561,0.0008779512,0.03005793,0.0008914232,0.5967297,0.3592733,0.0003267171],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.000553905,0.006474487,0.0147976,0.9724883,0.00474246,0.00004622643,0.0001427924,0.00006775581,0.0006866272],"genre_scores_gemma":[0.03834168,0.01620703,0.08499097,0.7814955,0.07594769,0.0007912538,0.000409865,0.0001702233,0.001645865],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.07540092,"threshold_uncertainty_score":0.3987629,"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."}}