{"id":"W191744706","doi":"10.1533/9781908818089.47","title":"Empirical analysis of drug approval * *This chapter is based upon material in M. Sawicka and R.A. Bouchard, ‘Empirical Analysis of Canadian Drug Approval Data 2001-2008: Are Canadian Pharmaceutical Players “Doing More With Less”?’ McGill Journal of Law & Health 3: 87-151 (2009).","year":2012,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drug; Drug development; Business; Actuarial science; Medicine; Pharmacology; Risk analysis (engineering); Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004466391,0.0005752953,0.0008782521,0.004666772,0.001118719,0.003483505,0.001034113,0.0009718483,0.01711204],"category_scores_gemma":[0.03393073,0.0005040118,0.0006559023,0.0128647,0.002543835,0.002668639,0.0008741162,0.002380385,0.002225417],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01092672,"about_ca_system_score_gemma":0.008113246,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2457247,"about_ca_topic_score_gemma":0.278288,"domain_scores_codex":[0.9956647,0.001156527,0.0002198549,0.0004450499,0.002146315,0.0003676751],"domain_scores_gemma":[0.9603143,0.03095578,0.003977096,0.001088531,0.003298751,0.0003656129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004487203,0.0001341987,0.06143611,0.001116684,0.000181296,0.0003306268,0.0008707791,0.009641166,0.0001551331,0.6010902,0.1284875,0.1965115],"study_design_scores_gemma":[0.0000232139,0.00007984236,0.2336677,0.002406487,0.0001855982,0.0004893381,0.002790108,0.03051892,0.001163846,0.2695023,0.4590357,0.0001368755],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0951842,0.2473232,0.05341873,0.05447808,0.0009168784,0.0002665585,0.02563003,0.0004184966,0.5223639],"genre_scores_gemma":[0.7521474,0.1345728,0.01379315,0.003955487,0.001232337,0.0001854487,0.01567475,0.0002366615,0.07820194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9890733,"threshold_uncertainty_score":0.4885888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1399775588601517,"score_gpt":0.3333490565088204,"score_spread":0.1933714976486687,"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."}}