{"id":"W3123155668","doi":"","title":"Clinical Trials for New Drug Development: Optimal Investment and Application","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interim; Clinical trial; Revenue; Interim analysis; Drug development; Investment (military); Net present value; Value (mathematics); Actuarial science; Medicine; Test (biology); Computer science; Drug; Operations management; Business; Economics; Finance; Microeconomics; Pharmacology; Production (economics)","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":[],"consensus_categories":[],"category_scores_codex":[0.01353629,0.001588156,0.002973341,0.001957684,0.001200746,0.007260602,0.001779137,0.005161677,0.01183816],"category_scores_gemma":[0.06161813,0.001976221,0.001018126,0.002774974,0.004138826,0.007800872,0.002360808,0.004745682,0.001083715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009067356,"about_ca_system_score_gemma":0.008572423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003618665,"about_ca_topic_score_gemma":0.003101957,"domain_scores_codex":[0.9903753,0.006856158,0.0002520017,0.0008154054,0.001088308,0.0006127909],"domain_scores_gemma":[0.9597116,0.03387201,0.003235326,0.001104541,0.001122867,0.0009536492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002252843,0.0001630178,0.001416644,0.0003648431,0.00008378764,0.0001326775,0.0001136236,0.2385403,0.0002480932,0.6939858,0.00830045,0.0564255],"study_design_scores_gemma":[0.0001838289,0.000145289,0.0007134909,0.0002316542,0.00004878497,0.00009277037,0.00008297424,0.2206041,0.0001918888,0.7691815,0.008485751,0.00003785847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05939288,0.01993711,0.721911,0.06055168,0.0005221243,0.00191021,0.001431505,0.0006671865,0.1336764],"genre_scores_gemma":[0.8550147,0.01248188,0.1185285,0.001772336,0.0005909128,0.001204615,0.0003827687,0.0001006273,0.009923575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01353629,"threshold_uncertainty_score":0.07158762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1302926225768785,"score_gpt":0.3791116526175856,"score_spread":0.248819030040707,"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."}}