{"id":"W2432199245","doi":"10.2196/resprot.5621","title":"Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study","year":2016,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences","keywords":"Repurposing; Drug repositioning; Social media; Drug; Drug development; Medicine; Data science; Computer science; Pharmacology; World Wide Web; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005866925,0.0002631082,0.0003692038,0.0003111444,0.001364851,0.000143753,0.001460794,0.0001953507,0.0006608976],"category_scores_gemma":[0.0009257543,0.000202624,0.0001133925,0.0005745816,0.0003401288,0.0006935581,0.001063515,0.0008792261,0.0002330734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000378018,"about_ca_system_score_gemma":0.0005399378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003392703,"about_ca_topic_score_gemma":0.0001211559,"domain_scores_codex":[0.9948934,0.001671013,0.0005341634,0.001033799,0.0007059526,0.001161679],"domain_scores_gemma":[0.9966877,0.001267095,0.000142217,0.0008617032,0.000532259,0.0005091043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.006190514,0.00534825,0.02081472,0.0002260396,0.0002584468,0.0001084176,0.002994945,0.00002241089,0.7472064,0.0001161123,0.2016813,0.01503236],"study_design_scores_gemma":[0.01350586,0.0004942438,0.009791777,0.0003277904,0.00008505605,0.00001889916,0.001708896,0.0009034689,0.04775928,0.001175836,0.9235752,0.0006537419],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.3957592,0.000007399239,0.0003014275,0.002465525,0.0002424959,0.5988153,0.001931445,0.0001825005,0.0002947423],"genre_scores_gemma":[0.4681816,0.000001544071,0.0001561584,0.0001632296,0.001131753,0.5296513,0.00004848423,0.00004086135,0.0006250854],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.7218938,"threshold_uncertainty_score":0.9999352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8354165269847041,"score_gpt":0.7292692315110744,"score_spread":0.1061472954736298,"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."}}