{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006449688,0.00135326,0.001343276,0.001477904,0.000401091,0.001689899,0.001183619,0.001195744,0.00108126],"category_scores_gemma":[0.02506986,0.0006274458,0.001458622,0.0006640159,0.0005177813,0.000965478,0.0007523498,0.0008647096,0.0002024761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00200586,"about_ca_system_score_gemma":0.002225113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02167996,"about_ca_topic_score_gemma":0.01049642,"domain_scores_codex":[0.9970514,0.0021642,0.0001403466,0.0002259477,0.000266349,0.000151772],"domain_scores_gemma":[0.9767399,0.02020011,0.0009717389,0.000726085,0.001045514,0.0003165458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008267498,0.0001800978,0.01112383,0.00005875331,0.0003008905,0.00003062175,0.00003806293,0.9747968,0.0001787094,0.0005800763,0.0003769355,0.01150849],"study_design_scores_gemma":[0.00003857309,0.0001282917,0.001088953,0.00001208168,0.00003675236,0.00001046711,0.00000960443,0.9980503,0.00009600214,0.0004399517,0.00007979444,0.000009187776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452409,0.001219684,0.04441333,0.001701924,0.0001370078,0.0001966357,0.001008294,0.0006380877,0.005444246],"genre_scores_gemma":[0.9846441,0.0002201451,0.01355584,0.0002010617,0.00004894977,0.000150198,0.0006641959,0.0000321772,0.0004833363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02167996,"threshold_uncertainty_score":0.04310757,"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."}}