{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008881469,0.0007391195,0.0006539801,0.008418581,0.0007411686,0.0009914043,0.0009484523,0.0004496197,0.004192922],"category_scores_gemma":[0.02697192,0.0002868738,0.001129746,0.01233352,0.0005099924,0.0007318018,0.0007783441,0.0005798115,0.001070195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003907894,"about_ca_system_score_gemma":0.008076197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05637639,"about_ca_topic_score_gemma":0.0296496,"domain_scores_codex":[0.9843479,0.00217793,0.005661492,0.001876226,0.005014786,0.0009216471],"domain_scores_gemma":[0.9499438,0.008054631,0.01897769,0.003518145,0.0183191,0.001186702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000447602,0.00009581139,0.946086,0.0006554642,0.0001572977,0.0002917907,0.000451006,0.001335233,0.001036398,0.0006090619,0.01262009,0.03621417],"study_design_scores_gemma":[0.00004167214,0.0001288275,0.9867471,0.00005369365,0.0000781369,0.0001659565,0.0003572067,0.001586215,0.001400089,0.0001290072,0.009281767,0.00003024291],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7811295,0.0008195652,0.006328615,0.001068277,0.0001087365,0.002587135,0.1981137,0.0005242807,0.009320149],"genre_scores_gemma":[0.8364727,0.0005289725,0.007243321,0.0004125681,0.00007781858,0.002843811,0.1474917,0.00008816378,0.004840892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05637639,"threshold_uncertainty_score":0.1120965,"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."}}