{"id":"W2181067433","doi":"10.1002/cyto.a.22732","title":"A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes","year":2015,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"National Institute of Allergy and Infectious Diseases; National Institute of Biomedical Imaging and Bioengineering; Vlaamse regering; U.S. Public Health Service; National Cancer Institute; Ovarian Cancer Research Fund; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Lupus Research Alliance; International Society for Advancement of Cytometry; Canada's Michael Smith Genome Sciences Centre; National Heart, Lung, and Blood Institute; U.S. Department of Defense","keywords":"Flow cytometry; Peripheral blood mononuclear cell; Algorithm; Cytometry; Population; Benchmark (surveying); Identification (biology); Computer science; Medicine; Immunology; Biology; In vitro","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":[0.02334183,0.002317797,0.001244156,0.003337094,0.0009817324,0.002342077,0.002073688,0.002780052,0.001351895],"category_scores_gemma":[0.06372771,0.0004285456,0.001123342,0.001677616,0.001125517,0.001482164,0.002202685,0.001855525,0.0008360788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001684664,"about_ca_system_score_gemma":0.002963373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004744902,"about_ca_topic_score_gemma":0.002252167,"domain_scores_codex":[0.991042,0.005080036,0.0007475218,0.001351849,0.001436063,0.0003425932],"domain_scores_gemma":[0.9700176,0.02194641,0.001196129,0.002883357,0.003413499,0.0005429675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003273255,0.002018502,0.09056252,0.0009872576,0.001788989,0.0002415693,0.0003650682,0.6404707,0.01086434,0.01238761,0.01472234,0.2223179],"study_design_scores_gemma":[0.0002819311,0.001284497,0.01145713,0.0001060463,0.00008951072,0.0001345746,0.0001330122,0.9685915,0.008417547,0.007236124,0.002229943,0.00003817022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5314632,0.003734912,0.4335716,0.001936954,0.0004305978,0.002294906,0.01102068,0.008175165,0.007371995],"genre_scores_gemma":[0.6964575,0.0006833375,0.2827127,0.0005832184,0.00011465,0.002119795,0.01565216,0.0005802287,0.001096451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02334183,"threshold_uncertainty_score":0.1234449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1488719244516753,"score_gpt":0.4015902594677722,"score_spread":0.2527183350160969,"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."}}