{"id":"W4392795393","doi":"10.1200/po.23.00489","title":"Biomarker Inference and the Timing of Next-Generation Sequencing in a Multi-Institutional, Cross-Cancer Clinicogenomic Data Set","year":2024,"lang":"en","type":"article","venue":"JCO Precision Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute; Genentech; Swiss Re; Seagen; Mirati Therapeutics; Gilead Sciences; Sanofi; Memorial Sloan-Kettering Cancer Center; GlaxoSmithKline; American Association for Cancer Research; AstraZeneca; Bristol-Myers Squibb; Eli Lilly and Company; Pfizer; Amgen; Doris Duke Charitable Foundation","keywords":"Medicine; Biomarker; Prostate cancer; Oncology; Hazard ratio; Internal medicine; Cohort; Cancer; Disease; Biomarker discovery; Breast cancer; Confidence interval; Biology","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":[0.0009042491,0.0001073937,0.0001869809,0.00006394871,0.00006512313,0.00006688782,0.0002577003,0.0001694356,0.00003075251],"category_scores_gemma":[0.0007385761,0.00007678643,0.00004032421,0.00009360868,0.0002893897,0.00001603927,0.0004697348,0.0001090398,0.000004331517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001106037,"about_ca_system_score_gemma":0.0008785669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003405549,"about_ca_topic_score_gemma":0.001521772,"domain_scores_codex":[0.9989044,0.00009598832,0.0003826619,0.0004126532,0.00007300249,0.0001312678],"domain_scores_gemma":[0.999045,0.0003882898,0.00008817722,0.0003807731,0.00006416678,0.00003355161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005073584,0.00005406668,0.00439949,0.00005696862,0.00009330941,0.00001676555,0.0004283263,0.005175736,0.7924281,0.0007912657,0.002820362,0.1932282],"study_design_scores_gemma":[0.004538719,0.0003069494,0.007579042,0.0001876616,0.00009009365,0.00005920581,0.0001346331,0.7795948,0.02839744,0.0005511915,0.1782011,0.0003592107],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765835,0.01372495,0.007954445,0.000322069,0.0007286519,0.0002745072,0.0002271711,0.000005547224,0.0001791051],"genre_scores_gemma":[0.987267,0.01006592,0.001867869,0.0002640185,0.0001892522,0.00003901334,0.0002392119,0.00001049043,0.00005724402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7744191,"threshold_uncertainty_score":0.313126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3055400721972522,"score_gpt":0.4554132460340161,"score_spread":0.1498731738367639,"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."}}