{"id":"W2944068250","doi":"10.1186/s40425-019-0544-x","title":"Immune-enrichment of non-small cell lung cancer baseline biopsies for multiplex profiling define prognostic immune checkpoint combinations for patient stratification","year":2019,"lang":"en","type":"article","venue":"Journal for ImmunoTherapy of Cancer","topic":"Cancer Immunotherapy and Biomarkers","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Krembil Foundation; Université de Montréal; Institute for Research in Immunology and Cancer; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canadian Cancer Society Research Institute; Institute of Cancer Research; Institut Du Cancer de Montréal; Terry Fox Research Institute; Canada Foundation for Innovation","keywords":"Multiplex; Medicine; Immune system; Immune checkpoint; Profiling (computer programming); Lung cancer; Oncology; Risk stratification; Immunology; Immunotherapy; Internal medicine; Bioinformatics; Biology; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0003791315,0.0002758274,0.0003019031,0.0007552386,0.0001979047,0.0004103958,0.0001827426,0.00029048,0.001691729],"category_scores_gemma":[0.0004492448,0.0001100157,0.0001990869,0.0003919734,0.0001295305,0.0002131386,0.0002752077,0.0002736825,0.0003967938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002252928,"about_ca_system_score_gemma":0.0002399089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004914178,"about_ca_topic_score_gemma":0.001284911,"domain_scores_codex":[0.9997811,0.00003503034,0.000014842,0.00007880638,0.00005064358,0.00003958656],"domain_scores_gemma":[0.9998209,0.00005177804,0.00004287301,0.00001934699,0.00003153916,0.00003346279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000595825,0.000107543,0.1170685,0.0001720269,0.00007292291,0.000122229,0.00007770611,0.0007171786,0.861092,0.0001735434,0.00040665,0.01939403],"study_design_scores_gemma":[0.00005797886,0.0009389536,0.5526967,0.00005762206,0.0002969647,0.001717958,0.0003302013,0.01616085,0.4205763,0.0008098629,0.006334091,0.0000224644],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831444,0.001366063,0.01199937,0.000101056,0.00001407236,0.0001234921,0.001962675,0.0001660462,0.001122736],"genre_scores_gemma":[0.9874344,0.0003616537,0.009863228,0.00006878731,0.00001329026,0.000122709,0.001539808,0.00001515724,0.0005809111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001691729,"threshold_uncertainty_score":0.005659401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01728549087146756,"score_gpt":0.3113932583143684,"score_spread":0.2941077674429008,"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."}}