{"id":"W101727076","doi":"10.1177/135965350701200709","title":"Predicting HIV Coreceptor Usage on the Basis of Genetic and Clinical Covariates","year":2007,"lang":"en","type":"article","venue":"Antiviral Therapy","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"AIDS Vancouver; University of British Columbia","funders":"","keywords":"Receiver operating characteristic; Support vector machine; Univariate; Covariate; Population; Artificial intelligence; Sensitivity (control systems); Biology; Computational biology; Statistics; Medicine; Internal medicine; Mathematics; Multivariate statistics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006714858,0.00008996436,0.0001587134,0.00003492969,0.0001191006,0.000006826432,0.0001045174,0.00009410189,0.000296168],"category_scores_gemma":[0.00006863233,0.00005025586,0.00006690159,0.00006509898,0.0003203256,0.00001991676,0.00002578271,0.0001715554,0.0002199418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005952313,"about_ca_system_score_gemma":0.00001868002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005033742,"about_ca_topic_score_gemma":0.0000149683,"domain_scores_codex":[0.9991316,0.0002259841,0.0002198193,0.0001576345,0.00003527306,0.0002297256],"domain_scores_gemma":[0.9987711,0.000963801,0.00006719106,0.0001580107,0.00002476769,0.00001514074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006047361,0.0002564891,0.8493536,0.000001934406,0.0003059233,0.000006089022,0.0002011998,6.200885e-8,0.04171074,0.0003236165,0.0009001513,0.1063355],"study_design_scores_gemma":[0.001711459,0.001224531,0.8591233,0.00001679759,0.00001170224,0.000004575575,0.0001128795,0.000003073455,0.1317341,0.00008125559,0.005910669,0.00006557371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965866,0.001305785,0.00007388634,0.0002753602,0.0001274182,0.0002144997,0.00003715946,0.00001587434,0.001363465],"genre_scores_gemma":[0.9974952,0.001685759,0.0000847365,0.0001236961,0.00003477573,0.000004217851,0.00001660379,0.000007507651,0.0005475267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1062699,"threshold_uncertainty_score":0.3242832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0380916285824094,"score_gpt":0.3250282618337816,"score_spread":0.2869366332513722,"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."}}