{"id":"W2407055211","doi":"10.1158/1557-3265.pmsclingen15-ia15","title":"Abstract IA15: “Next-gen” genomics-based clinical trials","year":2016,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Druggability; Clinical trial; Genomics; Precision medicine; Profiling (computer programming); Computational biology; Bioinformatics; Medicine; Biology; Genome; Pathology; Genetics; Computer science; Gene","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":[0.02779216,0.001214653,0.001790336,0.0007970147,0.0006933652,0.004205018,0.001410373,0.004095803,0.01836985],"category_scores_gemma":[0.017947,0.0004096098,0.002666614,0.0007425669,0.001370169,0.001634178,0.002460069,0.004700857,0.005467643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002609875,"about_ca_system_score_gemma":0.006064365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007025958,"about_ca_topic_score_gemma":0.0009096196,"domain_scores_codex":[0.9817821,0.01456718,0.0006583233,0.001034123,0.001266228,0.0006920777],"domain_scores_gemma":[0.9874799,0.004048041,0.002166222,0.001440279,0.001423455,0.003442193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.1273095,0.005982358,0.008157994,0.006689251,0.00507921,0.0005726674,0.000342407,0.006321522,0.0138403,0.04231608,0.2664101,0.5169786],"study_design_scores_gemma":[0.1989298,0.08964527,0.01789133,0.006254998,0.002940441,0.001178191,0.0001971192,0.01029208,0.008764624,0.06191768,0.6017706,0.0002178927],"study_design_candidate":"randomized_trial","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1978713,0.1043262,0.09650434,0.2142854,0.03420198,0.06223733,0.0615725,0.005766101,0.2232348],"genre_scores_gemma":[0.5209392,0.02855674,0.1380318,0.1699242,0.007599274,0.06249783,0.03341559,0.000821842,0.03821363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02779216,"threshold_uncertainty_score":0.1469807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5029259291989813,"score_gpt":0.5949847621123466,"score_spread":0.09205883291336536,"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."}}