{"id":"W2017350615","doi":"10.1258/ijsa.2008.008223","title":"Can a clinical prediction tool guide HIV-testing decisions? Experience at a national hospital in Guatemala","year":2008,"lang":"en","type":"article","venue":"International Journal of STD & AIDS","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; Center for AIDS Research, University of Washington; National Institutes of Health","keywords":"Medicine; Logistic regression; Demographics; Human immunodeficiency virus (HIV); HIV diagnosis; Family medicine; Demography; Internal medicine; Viral load; Antiretroviral therapy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008261274,0.0005430487,0.0004175575,0.001130819,0.0009693303,0.001637689,0.001012907,0.0008592415,0.004222327],"category_scores_gemma":[0.04741465,0.0002760063,0.0003160416,0.001115318,0.000695897,0.001434505,0.001195824,0.001134585,0.001279348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002204668,"about_ca_system_score_gemma":0.003665049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03208702,"about_ca_topic_score_gemma":0.04377913,"domain_scores_codex":[0.9961241,0.002638031,0.0002724827,0.0002154143,0.0003677116,0.0003821203],"domain_scores_gemma":[0.9832297,0.01058644,0.001301155,0.000759644,0.002148932,0.001974152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003152655,0.0005541214,0.6534504,0.0002698941,0.00006295903,0.001668702,0.002885111,0.008539684,0.0003400556,0.001749583,0.03963928,0.2905249],"study_design_scores_gemma":[0.0003330896,0.002602126,0.6085646,0.003361193,0.0002814819,0.006144094,0.01732596,0.2763507,0.003147242,0.01358865,0.06780845,0.0004924136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.820547,0.004114866,0.02949058,0.1185131,0.0002754343,0.0006413726,0.001799606,0.001271127,0.02334687],"genre_scores_gemma":[0.9660333,0.001383513,0.02812305,0.002503078,0.00009173227,0.0001199535,0.0005810722,0.00006048083,0.001103866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03208702,"threshold_uncertainty_score":0.06380051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09666765420239812,"score_gpt":0.4313866475932852,"score_spread":0.3347189933908871,"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."}}