{"id":"W3167163899","doi":"10.1200/jco.2021.39.15_suppl.2570","title":"Mega- and meta-analyses of fecal metagenomic studies in predicting response to immune checkpoint inhibitors.","year":2021,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Metagenomics; Meta-analysis; Microbiome; Bacteroides thetaiotaomicron; Bacteroides; Medicine; Biology; Immune system; Computational biology; Microbiology; Immunology; Internal medicine; Bioinformatics; Genetics; Bacteria; Gene","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":[0.01984027,0.002657238,0.005079153,0.007219542,0.0007797378,0.002473583,0.00213788,0.001385,0.002206054],"category_scores_gemma":[0.02993588,0.001188606,0.01371125,0.006067173,0.0003976928,0.0009104455,0.002880589,0.002197783,0.0004415898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008039235,"about_ca_system_score_gemma":0.001838783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002907746,"about_ca_topic_score_gemma":0.004584752,"domain_scores_codex":[0.9875433,0.009129498,0.000897915,0.001539937,0.0006440027,0.000245338],"domain_scores_gemma":[0.9817135,0.01462617,0.001207295,0.001537698,0.0005190599,0.0003963484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.008081971,0.0002952778,0.2188386,0.02726915,0.5989952,0.0009795715,0.0007473325,0.0548995,0.01586492,0.002141937,0.007810048,0.06407651],"study_design_scores_gemma":[0.001831857,0.003836371,0.2307992,0.004159602,0.4497294,0.001529317,0.001440911,0.2453098,0.007861563,0.01376608,0.03916391,0.0005720885],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5629022,0.1935025,0.1685675,0.005298854,0.001726597,0.001924037,0.05810057,0.005253461,0.002724296],"genre_scores_gemma":[0.8587459,0.00839123,0.1155894,0.0008213291,0.0001830533,0.001211997,0.01383103,0.000734382,0.0004916957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01984027,"threshold_uncertainty_score":0.1049266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3543097193804371,"score_gpt":0.554297626154189,"score_spread":0.1999879067737518,"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."}}