{"id":"W2509481015","doi":"10.1371/journal.pone.0162074","title":"Influence of Using Different Databases and ‘Look Back’ Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Toronto; University of Calgary; Institute for Clinical Evaluative Sciences; University of Manitoba; Alberta Health Services","funders":"Canadian Institutes of Health Research; Alberta Innovates; University of Alberta; Heart and Stroke Foundation of Canada","keywords":"Medicine; Comorbidity; Concordance; Retrospective cohort study; Logistic regression; Proportional hazards model; Population; Demography; Database; Statistic; Duration (music); Odds ratio; Emergency medicine; Pediatrics; Internal medicine; Statistics; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001033042,0.0001168972,0.0003206317,0.00008313062,0.00007125364,0.00001297444,0.00008747655,0.0000154174,0.00001342564],"category_scores_gemma":[0.0001759859,0.00007309137,0.00002464416,0.00008692229,0.00008344261,0.000142859,0.0001140967,0.0000268344,0.000001406896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008561578,"about_ca_system_score_gemma":0.0001070533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001685378,"about_ca_topic_score_gemma":0.0001475629,"domain_scores_codex":[0.9990399,0.00002503953,0.0002293173,0.0002713413,0.0002018827,0.0002325355],"domain_scores_gemma":[0.9986778,0.0003297906,0.0001167103,0.0003461172,0.0003463506,0.0001831681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002184897,0.00538707,0.9495866,0.01296755,0.0005843826,9.796443e-7,0.0002276478,0.00001684302,0.02443732,0.0006367379,0.001005916,0.002964014],"study_design_scores_gemma":[0.005565999,0.001421326,0.9835548,0.005685349,0.0002743891,3.469291e-7,0.0001373472,0.0001348008,0.002882784,0.0001152427,0.0001012485,0.0001263005],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905009,0.000097659,0.0005602824,0.004646634,0.000004695303,0.003256151,0.0009024776,0.00002243163,0.000008823919],"genre_scores_gemma":[0.9897897,0.0001247147,0.008967099,0.0005079831,0.00002606983,0.0003252773,0.0002012606,0.00002078468,0.00003709217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03396823,"threshold_uncertainty_score":0.2980579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2101216205293804,"score_gpt":0.3702623818568626,"score_spread":0.1601407613274821,"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."}}