{"id":"W3158797457","doi":"10.1101/2021.04.27.441693","title":"Mega- and meta-analyses of fecal metagenomic studies assessing response to immune checkpoint inhibitors","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre; Toronto General Hospital; University Health Network","funders":"Princess Margaret Cancer Foundation","keywords":"Metagenomics; Meta-analysis; Immunotherapy; Immune system; Microbiome; Biology; Mega-; Computational biology; Cancer immunotherapy; Immunology; Oncology; Internal medicine; Bioinformatics; Medicine; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.01420604,0.001698379,0.003980273,0.005203379,0.000631235,0.002438712,0.00136764,0.0009307071,0.001458169],"category_scores_gemma":[0.02100754,0.000864413,0.009400215,0.005090143,0.0003950406,0.0006335141,0.002030693,0.001410577,0.0002340631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000715971,"about_ca_system_score_gemma":0.001343716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002674239,"about_ca_topic_score_gemma":0.003214381,"domain_scores_codex":[0.988663,0.007509134,0.0009867051,0.001832023,0.0007302269,0.0002788539],"domain_scores_gemma":[0.9865458,0.009086967,0.001468508,0.001901476,0.0006705382,0.0003265923],"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":[0.006271771,0.000162653,0.4186094,0.01552607,0.456295,0.0005662183,0.0005007638,0.02796957,0.02659342,0.00109033,0.003161121,0.04325362],"study_design_scores_gemma":[0.0007264838,0.002191994,0.5637885,0.003002611,0.3273764,0.001119096,0.001078119,0.06725717,0.01172466,0.005226806,0.01621041,0.0002978535],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8161074,0.09653258,0.05288158,0.001923186,0.0004743616,0.0005425911,0.02889112,0.00119531,0.001451943],"genre_scores_gemma":[0.9620142,0.004948847,0.02533801,0.0003337805,0.0000769929,0.0003415818,0.006520845,0.0002157948,0.0002100212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9960197,"threshold_uncertainty_score":0.07512963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0647081892829106,"score_gpt":0.3359764090402867,"score_spread":0.2712682197573761,"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."}}