{"id":"W1982237827","doi":"10.1080/10543406.2014.948961","title":"Scientific Factors and Current Issues in Biosimilar Studies","year":2014,"lang":"en","type":"article","venue":"Journal of Biopharmaceutical Statistics","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interchangeability; Comparability; Biosimilar; Risk analysis (engineering); Bioequivalence; Computer science; Selection (genetic algorithm); Quality (philosophy); Management science; Reliability engineering; Biochemical engineering; Medicine; Mathematics; Pharmacology; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.001258658,0.0001590932,0.0004627386,0.0001834846,0.0001042366,0.00004503788,0.0001452819,0.0001090474,0.0001256013],"category_scores_gemma":[0.001429306,0.0001003474,0.0000580562,0.0001868974,0.000869603,0.00006575575,0.00008318297,0.0005009636,0.000009816863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003462898,"about_ca_system_score_gemma":0.00002912189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001657182,"about_ca_topic_score_gemma":0.000003794173,"domain_scores_codex":[0.9985508,0.0003448317,0.0005587051,0.00017262,0.0000883841,0.0002846433],"domain_scores_gemma":[0.9985799,0.0008402553,0.000178167,0.00008847035,0.000233076,0.00008016603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007987977,0.001514904,0.1630856,0.0007379585,0.001047244,0.00009702754,0.0017142,0.000005375415,0.2410735,0.03856251,0.07054021,0.4808226],"study_design_scores_gemma":[0.003403358,0.0009766112,0.05341453,0.0003071092,0.0004921052,0.0001174688,0.0006000623,0.0004061212,0.1364844,0.01638784,0.7868848,0.0005255619],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8518429,0.1248746,0.01161894,0.003946097,0.00714262,0.0002325818,0.0002409637,0.00002277936,0.0000784684],"genre_scores_gemma":[0.9846112,0.005176179,0.009738022,0.0002241408,0.00009461679,7.266579e-7,0.00000446771,0.00001113533,0.0001394763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7163446,"threshold_uncertainty_score":0.409205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002988965490811,"score_gpt":0.4318734905208749,"score_spread":0.3315745939717938,"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."}}