{"id":"W2526188806","doi":"10.1056/nejmsb1607705","title":"Limits to Personalized Cancer Medicine","year":2016,"lang":"en","type":"article","venue":"New England Journal of Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":423,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Medicine; Personalized medicine; Precision medicine; MEDLINE; Cancer Medicine; Clinical Oncology; Data science; Cancer; Bioinformatics; Internal medicine; Pathology; Computer science","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":[0.0004238156,0.0001182645,0.0002666877,0.00008207573,0.0000281135,0.000003068326,0.0001719771,0.000054286,0.000414767],"category_scores_gemma":[0.0009698934,0.00006066084,0.00004659247,0.00007405547,0.0001033921,0.000002618636,0.00002468343,0.00006534312,0.000004293704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000302608,"about_ca_system_score_gemma":0.0001878068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001038037,"about_ca_topic_score_gemma":0.0001282284,"domain_scores_codex":[0.9991078,0.00003173209,0.0003202445,0.0001331123,0.0002334204,0.0001736603],"domain_scores_gemma":[0.999005,0.00009690216,0.0001686677,0.0001459978,0.0002213783,0.0003620831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007076522,0.00001212647,0.002501393,0.000005586146,0.00007067519,0.00002351223,0.0002879319,0.00001461167,0.5888655,0.0001689614,0.1750615,0.2322806],"study_design_scores_gemma":[0.02635532,0.002325421,0.00212798,0.0003660874,0.00007745357,0.0001788707,0.00006136203,9.347075e-7,0.01655135,0.0002439556,0.9516037,0.0001076264],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8573604,0.01473302,0.008208912,0.1160745,0.00207446,0.0001240034,0.00001429551,0.000004200578,0.001406216],"genre_scores_gemma":[0.9589851,0.0115362,0.0004864876,0.005255886,0.02012978,0.00000262537,0.000003865011,0.0000225757,0.003577426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7765421,"threshold_uncertainty_score":0.4541408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847316335071769,"score_gpt":0.2948330069746737,"score_spread":0.276359843623956,"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."}}