{"id":"W3158236003","doi":"10.1038/s41698-021-00171-6","title":"Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival","year":2021,"lang":"en","type":"article","venue":"npj Precision Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Cancer Institute; Pfizer; European Commission; Fondation ARC pour la Recherche sur le Cancer; Novartis Pharmaceuticals Corporation; Eli Lilly and Company","keywords":"Everolimus; Concordance; Axitinib; Oncology; TSC1; Progression-free survival; Internal medicine; Medicine; Precision medicine; Biomarker; Sunitinib; PI3K/AKT/mTOR pathway; Targeted therapy; Overall survival; Biology; Cancer; Pathology; Signal transduction","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":[0.0002451711,0.000229824,0.0002984011,0.00002435553,0.00009568347,0.00005118653,0.0003319834,0.0002277685,0.00001411716],"category_scores_gemma":[0.0005290958,0.0001405415,0.00007964086,0.0001845078,0.000148878,0.00001075061,0.0005150979,0.00007301176,0.000002054612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008637354,"about_ca_system_score_gemma":0.0006918539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001291864,"about_ca_topic_score_gemma":0.0001509965,"domain_scores_codex":[0.9983724,0.0001058035,0.0003682615,0.0005571025,0.0003300933,0.0002663552],"domain_scores_gemma":[0.9984894,0.0001526465,0.0001500618,0.0007386073,0.000302629,0.0001666035],"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":[0.03235514,0.002190593,0.1326575,0.00003772101,0.0008550671,0.00006967733,0.0004164132,0.001940869,0.119686,0.0004901094,0.02473081,0.6845701],"study_design_scores_gemma":[0.02704577,0.05330028,0.319596,0.0004518802,0.0003688847,0.0003726246,0.0004970903,0.01293124,0.09397396,0.004598507,0.4852919,0.001571871],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855992,0.001794554,0.005778229,0.0005826277,0.0002290622,0.001504979,0.0008343277,0.00001410969,0.003662951],"genre_scores_gemma":[0.9904166,0.001010989,0.007196622,0.0002336478,0.0001135697,0.0003267714,0.0002265102,0.00003275352,0.0004425696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6829982,"threshold_uncertainty_score":0.5731115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799393244976983,"score_gpt":0.3142395893566886,"score_spread":0.2962456569069188,"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."}}