{"id":"W3156859909","doi":"10.1016/j.ejca.2021.03.013","title":"The European Union and personalised cancer medicine","year":2021,"lang":"en","type":"article","venue":"European Journal of Cancer","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"European union; Limiting; Cancer; Precision medicine; Personalized medicine; Medicine; Risk analysis (engineering); Political science; Bioinformatics; Business; Pathology; Biology; Economic policy; Internal medicine; Engineering","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":[0.00106414,0.0000957447,0.0001089125,0.00001643122,0.0001168387,0.00003966726,0.0001573898,0.00001236638,0.00004956575],"category_scores_gemma":[0.0001693729,0.00006422564,0.00005687774,0.00005689363,0.0001254861,0.000003071367,0.00009246408,0.0001140779,0.000001720781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001917519,"about_ca_system_score_gemma":0.0001381826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002130594,"about_ca_topic_score_gemma":0.0001406877,"domain_scores_codex":[0.9988507,0.0005024818,0.0002472357,0.0001291275,0.0001383146,0.0001321556],"domain_scores_gemma":[0.999194,0.00003172985,0.0001993809,0.0001514699,0.0003169821,0.0001064183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001302308,0.00002906344,0.005663525,0.00002290906,0.0002983691,0.0003415799,0.000567303,0.0002975047,0.357761,0.0001550613,0.1244295,0.5103039],"study_design_scores_gemma":[0.001019963,0.0001716963,0.03236993,0.00008897685,0.00005955388,0.00008574186,0.0002373019,0.00000591955,0.007791358,0.0000193212,0.9580483,0.0001019101],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6575788,0.2779727,0.001033988,0.02383972,0.002197757,0.00008190981,0.00004055272,0.000005227823,0.03724926],"genre_scores_gemma":[0.9189529,0.0745466,0.00008669373,0.001203791,0.003134749,8.224147e-7,0.000005176616,0.00003533429,0.002033901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8336188,"threshold_uncertainty_score":0.2619046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131596444065745,"score_gpt":0.2617101666602155,"score_spread":0.250394202219558,"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."}}