{"id":"W2055205760","doi":"10.2196/resprot.2315","title":"The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions","year":2013,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Carleton University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Universidad Politécnica de Madrid; Canarie; Microsoft Research; Heart and Stroke Foundation of Canada","keywords":"World Wide Web; Computer science; Web page; Personally identifiable information; JavaScript; Workflow; Web application; Plug-in; Internet privacy; Database; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001840518,0.0001553072,0.0001757572,0.00008140325,0.001007249,0.0002813441,0.0004870957,0.00009690227,0.00002835445],"category_scores_gemma":[0.0004695207,0.0001044192,0.0001607121,0.0001606787,0.0003746836,0.0000106222,0.0001554962,0.0004226505,0.00006887956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297876,"about_ca_system_score_gemma":0.0007709637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009652958,"about_ca_topic_score_gemma":0.0002166601,"domain_scores_codex":[0.9974747,0.0004960148,0.0003215972,0.0004445413,0.0004952665,0.0007679206],"domain_scores_gemma":[0.9985214,0.0003497283,0.0001157291,0.000390309,0.0004388963,0.000183977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008058339,0.0003697083,0.0006020563,0.0019505,0.0001122339,0.000005180606,0.000700308,0.000001738358,0.1707737,0.0003902645,0.7724828,0.0518057],"study_design_scores_gemma":[0.001207486,0.0009357327,0.000470471,0.001144437,0.000001409861,0.00001452652,0.003110394,0.001633215,0.006382586,0.0000407142,0.9848986,0.0001604837],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.03972274,0.0006162417,0.0032602,0.05079892,0.0001948254,0.9034674,0.0001822359,0.0002867453,0.001470768],"genre_scores_gemma":[0.1007702,0.000006338402,0.001611993,0.0002277109,0.0003665902,0.8931342,0.00003989707,0.00003231289,0.00381074],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.2124157,"threshold_uncertainty_score":0.774705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2335661848216555,"score_gpt":0.5180854587097071,"score_spread":0.2845192738880516,"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."}}