{"id":"W2130934452","doi":"10.1111/hiv.12156","title":"A comparison of computational models with and without genotyping for prediction of response to second‐line <scp>HIV</scp> therapy","year":2014,"lang":"en","type":"article","venue":"HIV Medicine","topic":"HIV/AIDS drug development and treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"AIDS Vancouver","funders":"Institute of Infection and Immunity; National Institute of Allergy and Infectious Diseases; U.S. Public Health Service; Università degli Studi di Brescia; Core Research for Evolutional Science and Technology; University of Cape Town; University of New South Wales; U.S. Department of Defense; Gilead Sciences; National Cancer Institute; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Genotyping; Genotype; Medicine; Regimen; Viral load; Internal medicine; Reverse-transcriptase inhibitor; Human immunodeficiency virus (HIV); Oncology; Antiretroviral therapy; Virology; Genetics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005837822,0.0001645101,0.0006004769,0.0002332978,0.00005167901,0.000002603029,0.0000443364,0.00005271018,0.00001792894],"category_scores_gemma":[0.000153095,0.0001099056,0.00003058413,0.0001653859,0.0001213679,0.0000458988,0.00001368341,0.0000701996,0.000001922805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003133986,"about_ca_system_score_gemma":0.00009389617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003246114,"about_ca_topic_score_gemma":0.000006025496,"domain_scores_codex":[0.9987751,0.0000598044,0.0004403333,0.0002332582,0.0003304617,0.0001610519],"domain_scores_gemma":[0.9987939,0.0004626,0.0001758365,0.0001585103,0.0002604812,0.0001486643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02730203,0.001864451,0.7566869,0.001477062,0.001922649,0.00000795044,0.06733546,0.01436376,0.06276613,0.002568057,0.04471849,0.01898706],"study_design_scores_gemma":[0.0759992,0.01652878,0.3940699,0.003451356,0.000891972,0.0001128358,0.004022343,0.4230472,0.03207266,0.002217975,0.04731144,0.0002743039],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8378738,0.0003370947,0.1577065,0.002650484,0.00003568324,0.0008408494,0.00005023812,0.00003073978,0.0004746119],"genre_scores_gemma":[0.9689193,0.00001621562,0.02787378,0.0002418896,0.00009923676,0.0000558452,0.0002210417,0.00002373749,0.002548931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4086835,"threshold_uncertainty_score":0.4481821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04253075692393683,"score_gpt":0.3136006925623039,"score_spread":0.2710699356383671,"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."}}