{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06933361,0.0006523209,0.001132395,0.001630322,0.0007800496,0.002565466,0.001894983,0.001211512,0.000938445],"category_scores_gemma":[0.1105841,0.0007903923,0.001995772,0.002798415,0.0009440963,0.001652811,0.001732315,0.002324159,0.0002193429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216182,"about_ca_system_score_gemma":0.001442945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006649883,"about_ca_topic_score_gemma":0.006112173,"domain_scores_codex":[0.9650887,0.02240662,0.00227052,0.007324893,0.001908359,0.001000899],"domain_scores_gemma":[0.8247676,0.1245042,0.02850031,0.01753734,0.002208951,0.00248165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009315606,0.0000636305,0.9848513,0.00004994249,0.001123498,0.0001560808,0.0001226045,0.006498748,0.0003710931,0.0003021529,0.0003453355,0.00518413],"study_design_scores_gemma":[0.0001798897,0.0006510443,0.8822776,0.0001223395,0.001823083,0.001054618,0.0003294792,0.1061565,0.002159269,0.00298611,0.002162153,0.00009781562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714281,0.001479104,0.02173373,0.0007966001,0.00008815579,0.0000709271,0.00372758,0.0001343808,0.0005415457],"genre_scores_gemma":[0.9917366,0.000117868,0.005066799,0.0001533028,0.00002954517,0.00004463814,0.002726149,0.00002491574,0.0001001232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06933361,"threshold_uncertainty_score":0.3666756,"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."}}