{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07097247,0.0014087,0.003433757,0.002365842,0.003476905,0.0124559,0.003578913,0.01743556,0.01370815],"category_scores_gemma":[0.1074064,0.0008736931,0.002539488,0.001376773,0.04336204,0.02272212,0.01320116,0.03071382,0.004731682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007557531,"about_ca_system_score_gemma":0.009977972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002103928,"about_ca_topic_score_gemma":0.001189451,"domain_scores_codex":[0.9469643,0.02807179,0.002229961,0.005753951,0.01536573,0.001614304],"domain_scores_gemma":[0.8547533,0.1226002,0.002730166,0.01010388,0.005810789,0.004001764],"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":[0.0001118228,0.00007397139,0.0004365572,0.0005068327,0.0001162676,0.000132721,0.0007958296,0.001063682,0.0002095002,0.8790742,0.06111977,0.05635889],"study_design_scores_gemma":[0.00004572861,0.00005566299,0.0002384239,0.0005880881,0.00002685645,0.0002767814,0.0002856248,0.0004706768,0.00009659775,0.8098927,0.1879823,0.00004051547],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001677149,0.07004846,0.02110529,0.8559986,0.006615111,0.0000651582,0.000175454,0.0001517721,0.04416301],"genre_scores_gemma":[0.218665,0.110088,0.03491852,0.5751294,0.04154776,0.0008886858,0.000297934,0.0002922882,0.01817244],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.07097247,"threshold_uncertainty_score":0.3753428,"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."}}