{"id":"W2902579141","doi":"10.1172/jci.insight.121867","title":"A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies","year":2018,"lang":"en","type":"article","venue":"JCI Insight","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Terry Fox Research Institute; University of Manitoba; Shared Health; CancerCare Manitoba; Hôpital Maisonneuve-Rosemont; Université de Montréal; University of Alberta; BC Children's Hospital; Manitoba Health; Toronto General Hospital; University of Toronto; University of British Columbia","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research","keywords":"Biomarker; Immune system; Medicine; Computer science; Computational biology; Data science; Internal medicine; Medical physics; Immunology; Biology","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":[0.01580589,0.001462429,0.001460307,0.003492492,0.001123855,0.00260358,0.002148847,0.001107613,0.003581338],"category_scores_gemma":[0.01314634,0.0009190063,0.001002742,0.002728486,0.0009956592,0.001542485,0.003125726,0.00169074,0.002835996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130332,"about_ca_system_score_gemma":0.004428483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001493303,"about_ca_topic_score_gemma":0.001977431,"domain_scores_codex":[0.9887926,0.002956315,0.001304198,0.002585833,0.003717521,0.0006436582],"domain_scores_gemma":[0.9905615,0.001675328,0.001173798,0.002529083,0.00354488,0.0005154285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002451053,0.001416061,0.03166811,0.0009334484,0.0007109785,0.0004643824,0.0007504526,0.01240221,0.6629208,0.007684425,0.0313622,0.2472357],"study_design_scores_gemma":[0.0006215049,0.002080536,0.08015656,0.0003288628,0.0003999461,0.0009487439,0.0003237719,0.1144004,0.6986286,0.01091623,0.09064576,0.0005490662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04429505,0.0003640538,0.9227729,0.000384644,0.0002170346,0.003453206,0.008404045,0.01828571,0.001823411],"genre_scores_gemma":[0.08341246,0.000240401,0.8932711,0.000346152,0.0001257438,0.008333537,0.01193668,0.001154038,0.001179888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01580589,"threshold_uncertainty_score":0.08359051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08095879672373672,"score_gpt":0.3450819695409605,"score_spread":0.2641231728172238,"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."}}