{"id":"W3121538607","doi":"10.17848/wp19-306","title":"Medical Innovation, Education, and Labor Market Outcomes for Cancer Patients","year":2019,"lang":"en","type":"report","venue":"","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"Syddansk Universitet; Institut für Arbeitsmarkt- und Berufsforschung; W.E. Upjohn Institute for Employment Research","keywords":"Cancer; Educational attainment; Medicine; Prostate cancer; Breast cancer; Cancer treatment; Cancer registry; Work (physics); Economics; Internal medicine; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00155318,0.0001687885,0.0002629107,0.001253087,0.0005263797,0.001197754,0.0003314372,0.0006337267,0.008761574],"category_scores_gemma":[0.007493745,0.0000870823,0.0006032877,0.002227011,0.0003769587,0.0006766333,0.0007134326,0.0009542234,0.000928721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001695605,"about_ca_system_score_gemma":0.002105156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06855772,"about_ca_topic_score_gemma":0.0918118,"domain_scores_codex":[0.9994425,0.0001257189,0.00003618815,0.00005102335,0.0001195199,0.0002250039],"domain_scores_gemma":[0.9929568,0.001939188,0.003438285,0.0001602712,0.0004373465,0.00106813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008156228,0.0001769474,0.9870243,0.000022225,0.00004705783,0.00003840338,0.0001537176,0.0009111808,0.00003146864,0.001102217,0.003213,0.007197884],"study_design_scores_gemma":[0.00003514473,0.0001042817,0.9932131,0.00003707771,0.00004362793,0.0000400671,0.0005404933,0.001594478,0.00009106015,0.0009963041,0.003294422,0.00001002009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708946,0.001455291,0.000353956,0.006537355,0.00005096069,0.00005178113,0.008288577,0.00001591661,0.01235163],"genre_scores_gemma":[0.9923652,0.0005886842,0.00009590432,0.000359933,0.0001162816,0.00003068533,0.003539566,0.000004401651,0.002899337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06855772,"threshold_uncertainty_score":0.1363173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06855752737312243,"score_gpt":0.3701336889775521,"score_spread":0.3015761616044297,"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."}}