{"id":"W4390266465","doi":"10.2196/46413","title":"Identification of Hypertension in Electronic Health Records Through Computable Phenotype Development and Validation for Use in Public Health Surveillance: Retrospective Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention","keywords":"Medicine; Public health surveillance; Population; Health information exchange; Public health; Electronic health record; Health care; Cohort; Population health; Family medicine; Pediatrics; Gerontology; Demography; Environmental health; Internal medicine; Health information","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02578352,0.0006858059,0.0006399644,0.003660634,0.000838132,0.001625704,0.001386462,0.0007317641,0.0007781505],"category_scores_gemma":[0.08295496,0.0009196536,0.001026767,0.00414623,0.001272393,0.00172829,0.001798061,0.0008421803,0.0003523477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159667,"about_ca_system_score_gemma":0.002081227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008278567,"about_ca_topic_score_gemma":0.006093038,"domain_scores_codex":[0.9708121,0.01356675,0.005533694,0.003686034,0.005391461,0.001009794],"domain_scores_gemma":[0.9079344,0.02958515,0.02952868,0.01745281,0.01410336,0.0013957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006124432,0.00004693638,0.998171,0.00002667846,0.00005122076,0.00002972444,0.0001906274,0.00004938759,0.00005272586,0.00003495138,0.00007776083,0.001207773],"study_design_scores_gemma":[0.00003985382,0.0003843627,0.9960138,0.00006862005,0.0001418759,0.0003976385,0.0005542741,0.001417449,0.0003252937,0.0000624181,0.0005796682,0.00001481821],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935635,0.0004014537,0.003727825,0.00003914439,0.00001149368,0.0004954432,0.001176648,0.00001691946,0.0005675812],"genre_scores_gemma":[0.9953215,0.0001533876,0.002319,0.00006544942,0.00001959588,0.0004334352,0.001617666,0.00001042908,0.00005972232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02578352,"threshold_uncertainty_score":0.136358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3062029594468191,"score_gpt":0.5219362269234643,"score_spread":0.2157332674766452,"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."}}