{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001517442,0.001233101,0.00115591,0.0009841298,0.0002687384,0.001109021,0.0009313634,0.0006624859,0.001955441],"category_scores_gemma":[0.00376273,0.0003171388,0.0008384531,0.0006270488,0.0004491823,0.0007448072,0.0008608252,0.0009129111,0.0005479215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005665759,"about_ca_system_score_gemma":0.001309529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003179455,"about_ca_topic_score_gemma":0.002856277,"domain_scores_codex":[0.9995653,0.0001121329,0.00002717404,0.0001522099,0.0001013047,0.00004190654],"domain_scores_gemma":[0.9989418,0.0005990741,0.0001364402,0.0001198196,0.0001503146,0.00005254197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008187213,0.0001663856,0.01998888,0.0001415796,0.0002751909,0.0001359314,0.0000646569,0.7227569,0.007695269,0.005815018,0.005022995,0.2371185],"study_design_scores_gemma":[0.00003545385,0.0001397,0.0009566092,0.00001119809,0.00004106495,0.00004286076,0.000007600614,0.9906347,0.002043554,0.005051263,0.001022515,0.00001339572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04567692,0.0003220217,0.9468434,0.0005147144,0.00007408378,0.000133914,0.001168382,0.003877852,0.001388602],"genre_scores_gemma":[0.6330033,0.0004119912,0.3600789,0.0004931447,0.0001743897,0.0005798406,0.002531956,0.0002356286,0.002490817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003179455,"threshold_uncertainty_score":0.00802511,"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."}}