{"id":"W2596575759","doi":"10.1287/msom.2017.0616","title":"Clinical Trials for New Drug Development: Optimal Investment and Application","year":2017,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interim; Clinical trial; Revenue; Interim analysis; Drug development; Investment (military); Value (mathematics); Computer science; Net present value; Actuarial science; Medicine; Test (biology); Operations management; Operations research; Drug; Business; Economics; Finance; Microeconomics; Mathematics; Production (economics); Pharmacology","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":[0.002307624,0.0001696047,0.0004261303,0.0001087305,0.0006154268,0.0006138172,0.0004113574,0.00005800789,0.00007797356],"category_scores_gemma":[0.00006604728,0.0001872196,0.00008874347,0.00002151384,0.0000303446,0.0003215149,0.0002932486,0.00009359365,0.0003150001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007480951,"about_ca_system_score_gemma":0.00002329436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002879602,"about_ca_topic_score_gemma":0.0002815897,"domain_scores_codex":[0.9981428,0.00002455302,0.001061006,0.0004986742,0.00002657162,0.0002463602],"domain_scores_gemma":[0.998785,0.0000747973,0.0003567952,0.0005704997,0.00001671896,0.0001962067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007700418,0.0002854076,0.002200889,0.000359764,0.0007253441,0.00000160321,0.001128317,0.007785516,0.000007603713,0.786971,0.01093597,0.1895216],"study_design_scores_gemma":[0.001892427,0.00001665947,0.03255859,0.00001630518,0.00004078191,5.409299e-7,0.0000515504,0.03175749,0.0005292389,0.01329124,0.9195138,0.0003314089],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6981001,0.0008475234,0.1596443,0.06247317,0.001507869,0.006140241,0.0002261098,0.0001570778,0.07090359],"genre_scores_gemma":[0.8775868,0.0007979854,0.08913968,0.01894681,0.0008633756,0.0009044143,0.0001631249,0.00006727997,0.01153057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9085778,"threshold_uncertainty_score":0.7634594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1940947930245319,"score_gpt":0.3905905963536664,"score_spread":0.1964958033291345,"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."}}