{"id":"W7145223351","doi":"","title":"各国の副作用報告データベースを活用した副作用発現リスク因子の国際的地域差の解析","year":2019,"lang":"ja","type":"report","venue":"Institutional Repositories DataBase (IRDB)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adverse effect; Polypharmacy; Event (particle physics); Risk assessment; Pharmacovigilance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.007757063,0.0003482389,0.0005831704,0.004088109,0.001172425,0.003201546,0.000869013,0.000579462,0.005935873],"category_scores_gemma":[0.02920883,0.000311303,0.001301438,0.004107486,0.00120412,0.004197175,0.001360669,0.0008094966,0.0006266751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002310811,"about_ca_system_score_gemma":0.00340468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084491,"about_ca_topic_score_gemma":0.01935569,"domain_scores_codex":[0.9907746,0.002944533,0.002014881,0.001388635,0.002425037,0.0004524476],"domain_scores_gemma":[0.9683143,0.01363572,0.007992445,0.002448111,0.006562516,0.00104692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005343514,0.0001667491,0.7861034,0.001501157,0.001466134,0.0005360935,0.003087181,0.0007156265,0.00106496,0.008094009,0.002517163,0.1942131],"study_design_scores_gemma":[0.00008162994,0.000340976,0.9453035,0.0009922584,0.001614416,0.002126387,0.005867054,0.001905482,0.003783643,0.0153555,0.02253274,0.00009637244],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8821041,0.02915713,0.02022542,0.008246511,0.0003923761,0.0004875494,0.005388302,0.0000943706,0.05390424],"genre_scores_gemma":[0.9882145,0.003915223,0.003898605,0.0009885692,0.0001579882,0.0001235486,0.0008953466,0.00001500618,0.001791204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01084491,"threshold_uncertainty_score":0.04102379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04221366938867643,"score_gpt":0.3047930223849062,"score_spread":0.2625793529962298,"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."}}