{"id":"W4393986694","doi":"10.1158/1538-7445.am2024-lb395","title":"Abstract LB395: NetraAI-driven discovery of novel biomarkers in MSI-high colon cancer for precision immunotherapy","year":2024,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Immunotherapy and Biomarkers","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cancer; Medicine; Immunotherapy; Colorectal cancer; Microsatellite instability; Oncology; Biomarker discovery; Cancer immunotherapy; Internal medicine; Cancer research; Biology; Genetics; Proteomics","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":[0.001857692,0.0007548986,0.000861414,0.0008827885,0.0002586468,0.00113456,0.0008853345,0.0006294253,0.002194414],"category_scores_gemma":[0.001909751,0.0002823806,0.0007220914,0.0006063926,0.00035963,0.0006816341,0.0009724518,0.0009369752,0.0009199095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009048057,"about_ca_system_score_gemma":0.0006948519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001007148,"about_ca_topic_score_gemma":0.001124216,"domain_scores_codex":[0.9995086,0.0001163451,0.00002544303,0.0001479253,0.0001619992,0.00003953734],"domain_scores_gemma":[0.999332,0.0002418479,0.0001439013,0.00007148089,0.000148,0.00006281764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00233114,0.0007946003,0.04363825,0.0007521666,0.0005311854,0.000498944,0.0001155026,0.3642673,0.2694032,0.00786112,0.009618045,0.3001885],"study_design_scores_gemma":[0.00002899694,0.0003081739,0.002745153,0.000014739,0.00003731009,0.00008077815,0.00001157262,0.9596442,0.03285036,0.002451923,0.00180312,0.00002367628],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4514022,0.002687675,0.5253949,0.002257745,0.0003077237,0.0003841366,0.004018339,0.006523238,0.007023918],"genre_scores_gemma":[0.8745096,0.0005271362,0.1159607,0.0007408115,0.0001404272,0.0002759591,0.003750071,0.0001263245,0.003968997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002194414,"threshold_uncertainty_score":0.009824574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08902612866333698,"score_gpt":0.4406809275490307,"score_spread":0.3516547988856937,"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."}}