{"id":"W2559958825","doi":"","title":"[Future Perspective of Pharmacoepidemiology in the \"Big Data Era\" and the Growth of Information Sources].","year":2016,"lang":"en","type":"article","venue":"PubMed","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Pharmacoepidemiology; Enthusiasm; Observational study; Big data; Confusion; Data science; Perspective (graphical); Medicine; Computer science; Risk analysis (engineering); Data mining; Psychology; Pharmacology; Medical prescription; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.001227842,0.00005164712,0.000114583,0.00002250788,0.00001757776,0.000002912732,0.000341408,0.00007844014,9.509629e-7],"category_scores_gemma":[0.001370768,0.00002041021,0.00002206851,0.00005676395,0.0004850284,0.000004937371,0.0001450311,0.00005847184,2.240793e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003357459,"about_ca_system_score_gemma":0.00001694685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000946541,"about_ca_topic_score_gemma":0.00002861793,"domain_scores_codex":[0.9993565,0.0001993851,0.0001689607,0.00009864279,0.00006195119,0.0001145541],"domain_scores_gemma":[0.9994016,0.0001862789,0.0001086852,0.0002444762,0.00004267847,0.00001627235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007377616,0.00005008831,0.02288118,0.00004388392,0.000123148,5.110949e-7,0.002020846,3.775528e-7,0.00213353,0.006698241,0.0110637,0.9542468],"study_design_scores_gemma":[0.007369766,0.0001633745,0.8466953,0.0000217335,0.00008688428,0.00003644529,0.007655911,0.00003625674,0.01585077,0.007147336,0.1147079,0.0002282642],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9530823,0.004815906,0.001967777,0.03755543,0.0002832125,0.0005419129,0.0001189769,0.000006973648,0.001627556],"genre_scores_gemma":[0.9983841,0.0007779651,0.00007504627,0.0005077171,0.0001623823,0.00006101145,0.000014896,0.000001662767,0.0000151719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9540185,"threshold_uncertainty_score":0.1787107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03400357232454273,"score_gpt":0.271121271288521,"score_spread":0.2371176989639783,"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."}}