{"id":"W2536899752","doi":"10.2196/medinform.6437","title":"Population Analysis of Adverse Events in Different Age Groups Using Big Clinical Trials Data","year":2016,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adverse effect; Clinical trial; Medicine; Population; Adverse drug event; Intensive care medicine; Internal medicine; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04764453,0.001000472,0.002012353,0.006028723,0.0005522373,0.002180816,0.001433293,0.001052252,0.002257416],"category_scores_gemma":[0.10044,0.0005022065,0.004630107,0.007402289,0.0007421771,0.001865037,0.00174375,0.001682567,0.0003680589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008345923,"about_ca_system_score_gemma":0.002044115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001559824,"about_ca_topic_score_gemma":0.001894847,"domain_scores_codex":[0.9484044,0.03085087,0.009350551,0.005477224,0.005053952,0.0008629453],"domain_scores_gemma":[0.8275173,0.1040488,0.04502717,0.01497481,0.006197403,0.002234495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001680239,0.0001864512,0.9321642,0.001782582,0.009481876,0.0003772245,0.0003966848,0.01292125,0.0006050619,0.002000462,0.004084281,0.03431959],"study_design_scores_gemma":[0.0007762655,0.001717192,0.876877,0.0008687633,0.008041915,0.001372649,0.0007934046,0.0749151,0.001757027,0.02004994,0.01266894,0.0001618242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7170616,0.0137303,0.1751692,0.004242206,0.0004717463,0.003690092,0.0800706,0.0009668373,0.004597481],"genre_scores_gemma":[0.9313799,0.001324622,0.03755267,0.0006665135,0.0002137375,0.003008211,0.02547844,0.00006746523,0.0003083946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04764453,"threshold_uncertainty_score":0.2519714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8073965431513741,"score_gpt":0.6622115404118676,"score_spread":0.1451850027395065,"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."}}