{"id":"W2249509298","doi":"10.1093/pubmed/fdv155","title":"Methods of defining hypertension in electronic medical records: validation against national survey data","year":2015,"lang":"en","type":"article","venue":"Journal of Public Health","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Alberta Health Services; University of Alberta; Alberta Health; University of Calgary","funders":"Canadian Institutes of Health Research; Cumming School of Medicine, University of Calgary; Health Research Board; University of Calgary","keywords":"Medicine; Blood pressure; Medical prescription; Medical record; Diagnosis code; Antihypertensive drug; Prevalence; Emergency medicine; Internal medicine; Pediatrics; Intensive care medicine; Epidemiology; Environmental health; Population","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1622151,0.001089878,0.0009592221,0.007169086,0.0009787505,0.002909286,0.00280952,0.001271164,0.001909351],"category_scores_gemma":[0.3401381,0.0007483941,0.001959159,0.006475535,0.001463984,0.003045115,0.003425472,0.001106957,0.0009343993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001847008,"about_ca_system_score_gemma":0.003624924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008560803,"about_ca_topic_score_gemma":0.00727863,"domain_scores_codex":[0.8172168,0.1145451,0.03084625,0.008946203,0.02660898,0.0018367],"domain_scores_gemma":[0.6069923,0.1965497,0.08508226,0.055531,0.054242,0.00160279],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004011704,0.0004228892,0.9720573,0.0005521422,0.0006443756,0.00004102848,0.0008663632,0.001116808,0.0002126505,0.0005357778,0.001186106,0.02196336],"study_design_scores_gemma":[0.0002599086,0.0008825082,0.9720409,0.001081005,0.0004189813,0.0003708912,0.00106681,0.01584786,0.001172401,0.000886121,0.005903965,0.00006866374],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8531384,0.002112479,0.09307475,0.000952579,0.0004093406,0.01449648,0.02354892,0.0005234969,0.01174354],"genre_scores_gemma":[0.9101601,0.0006374923,0.06662691,0.0003741883,0.0001420917,0.006859135,0.0144453,0.00009597055,0.0006588029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8377849,"threshold_uncertainty_score":0.8578855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5991402303487797,"score_gpt":0.5864602293305592,"score_spread":0.01268000101822053,"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."}}