{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.5610845,0.001760271,0.00512449,0.008448251,0.002959169,0.01552354,0.007715438,0.01743539,0.004765478],"category_scores_gemma":[0.5744447,0.001693927,0.003871867,0.01266201,0.03083174,0.02717341,0.006385346,0.01448078,0.001672906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0108235,"about_ca_system_score_gemma":0.01829188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001879688,"about_ca_topic_score_gemma":0.001591125,"domain_scores_codex":[0.4809856,0.388838,0.06526487,0.01430739,0.04856063,0.002043539],"domain_scores_gemma":[0.1420133,0.7944925,0.01753233,0.01150263,0.03181252,0.002646668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009488325,0.000353511,0.003094928,0.02476808,0.0005803914,0.000358416,0.002170328,0.001751082,0.0003836497,0.3127807,0.03470388,0.6181062],"study_design_scores_gemma":[0.0005406652,0.001007128,0.004301359,0.04071569,0.0007006323,0.001025522,0.003068686,0.002420881,0.0008393708,0.663237,0.2819001,0.0002429118],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001409936,0.7512687,0.01691486,0.2169943,0.008773877,0.0002553157,0.0001409706,0.00007202967,0.00417],"genre_scores_gemma":[0.07230704,0.6185044,0.08375923,0.136812,0.08399363,0.002303549,0.0003195802,0.0002063331,0.001794267],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5610845,"threshold_uncertainty_score":0.541261,"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."}}