{"id":"W6976988048","doi":"10.60692/dcr9n-xfv56","title":"Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Psychotherapy Techniques and Applications","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Angiogenesis; Tumor cells; Endothelial stem cell; Cell growth; Chemotherapy; Reinforcement learning; Vascular endothelial growth factor","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":[0.0001767962,0.0001355109,0.0002427567,0.0001840967,0.0002636407,0.00001568536,0.0001062668,0.00003051775,0.00003083049],"category_scores_gemma":[0.000001129471,0.00009332898,0.00009545885,0.000136761,0.00001997126,0.00005350413,0.00002780571,0.00007702184,0.000003423982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008980483,"about_ca_system_score_gemma":0.00001223805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005984433,"about_ca_topic_score_gemma":6.28491e-8,"domain_scores_codex":[0.9988648,0.0001030691,0.0005713465,0.0001238105,0.00020969,0.0001272578],"domain_scores_gemma":[0.9993821,0.00002110435,0.0002978808,0.0002191146,0.00005272343,0.00002706576],"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":[0.00009320558,0.00002630519,0.003851636,0.00002644688,0.00009156469,1.794669e-7,0.07188948,0.9224049,0.00001425121,0.0007911783,0.000001127999,0.0008096746],"study_design_scores_gemma":[0.000944899,0.0001533239,0.0008981496,0.00003543841,0.00003229724,0.00001893518,0.007157981,0.9905941,0.00005792976,0.0000102206,0.00001242613,0.00008423143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8106495,0.00001980176,0.1885191,0.00001981901,0.00005421485,0.0003913735,0.0001205021,0.00005252335,0.000173244],"genre_scores_gemma":[0.9994183,7.469764e-7,0.0003609452,0.00002735207,0.00001975796,0.0001073003,0.00003795408,0.00001211483,0.00001557981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1887688,"threshold_uncertainty_score":0.3805845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0634961057690118,"score_gpt":0.2975497590822739,"score_spread":0.2340536533132621,"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."}}