{"id":"W2768977396","doi":"10.1016/j.cell.2017.10.032","title":"Core Clinical Data Elements for Cancer Genomic Repositories: A Multi-stakeholder Consensus","year":2017,"lang":"en","type":"article","venue":"Cell","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Human Genome Research Institute; Foundation Medicine; Genentech; Actuate Therapeutics; XBiotech; Genomic Health; Natera; Exelixis; Merck; Amgen; Cancer Research Society; American Association for Cancer Research","keywords":"Biology; Stakeholder; Core (optical fiber); Computational biology; Set (abstract data type); Consensus conference; Data science; Bioinformatics; Library science; Computer science; Public relations; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.385406,0.0016094,0.003864335,0.008647218,0.007424362,0.02859045,0.01775113,0.01971524,0.007892496],"category_scores_gemma":[0.2909612,0.002666481,0.004146687,0.007880446,0.01118483,0.03871123,0.03056935,0.02761081,0.002670814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01536551,"about_ca_system_score_gemma":0.08762535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01012459,"about_ca_topic_score_gemma":0.01212499,"domain_scores_codex":[0.8293917,0.07242259,0.03442596,0.01268907,0.04044623,0.01062449],"domain_scores_gemma":[0.4229248,0.2813582,0.01524838,0.06269754,0.1762066,0.04156449],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004849264,0.0004660753,0.008517786,0.004535792,0.000601788,0.001718123,0.02227125,0.004350674,0.006985988,0.3717027,0.1990848,0.3792801],"study_design_scores_gemma":[0.0002177589,0.0002017249,0.004789668,0.01430726,0.0004058488,0.001800823,0.0140204,0.008043506,0.004598789,0.2705235,0.6806953,0.0003953142],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.01375528,0.01520174,0.2396768,0.6998656,0.004063949,0.001357873,0.0009804892,0.001634882,0.02346337],"genre_scores_gemma":[0.2096354,0.0105788,0.6213237,0.1367317,0.003592579,0.001800105,0.005344658,0.002093216,0.00889977],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.614594,"threshold_uncertainty_score":0.7579039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2646353651656412,"score_gpt":0.4139010134237416,"score_spread":0.1492656482581003,"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."}}