{"id":"W2347782313","doi":"","title":"Data Characteristics Analysis of Adverse Drug Reaction/Event Report Form in China","year":2010,"lang":"en","type":"article","venue":"Chinese Journal of Pharmacovigilance","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Warning system; Adverse drug reaction; China; Event (particle physics); Drug reaction; Early warning system; Data mining; Computer science; Adverse effect; Adverse Event Reporting System; Risk analysis (engineering); Database; Drug; Medicine; Pharmacology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002999856,0.0001901706,0.0006307983,0.0007535185,0.00004403145,0.00002936106,0.00196238,0.00003808513,0.00002424376],"category_scores_gemma":[0.0007590682,0.0001490052,0.0002365462,0.001929646,0.00005024025,0.001789866,0.0004455372,0.0006307011,0.000001900876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005995768,"about_ca_system_score_gemma":0.0003082022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003008964,"about_ca_topic_score_gemma":0.0000571479,"domain_scores_codex":[0.997424,0.0001298052,0.001273892,0.0003280691,0.0006468701,0.0001973337],"domain_scores_gemma":[0.9966053,0.0003724339,0.00166814,0.0009171824,0.0002994421,0.0001374997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003121298,0.001271703,0.8119901,0.0001449461,0.001580034,0.002111936,0.002231609,0.04533987,0.1079476,0.001600808,0.001117638,0.02435163],"study_design_scores_gemma":[0.0004340291,0.0000126581,0.6886779,0.0000231122,0.0001364558,0.0003245563,0.000005075346,0.3073692,0.0004893365,0.001368321,0.00103108,0.0001282577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9258277,0.00005220133,0.07153533,0.000543711,0.001684041,0.00008255294,0.00006866446,0.00001358957,0.0001922161],"genre_scores_gemma":[0.9758255,0.00006062411,0.02370389,0.00008443274,0.0002295618,0.000001519621,0.00004334463,0.000009299436,0.00004183498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2620293,"threshold_uncertainty_score":0.6076257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966842044202744,"score_gpt":0.3786612943079714,"score_spread":0.3589928738659439,"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."}}