{"id":"W2944302739","doi":"10.14740/jocmr3830","title":"Phenotyping to Facilitate Accrual for a Cardiovascular Intervention","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Medicine Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Human Genome Research Institute; National Institutes of Health","keywords":"Medicine; Informatics; Annotation; Intervention (counseling); Cohort; Accrual; Health informatics; Electronic health record; Computer science; Health care; Public health; Artificial intelligence; Pathology; Nursing","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":[0.09190206,0.0009481117,0.0009680216,0.006838938,0.001772625,0.004090047,0.003193562,0.001266063,0.008589005],"category_scores_gemma":[0.1565248,0.0007498089,0.001054408,0.003801335,0.0007039506,0.002817895,0.005547957,0.00226612,0.003263538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002967455,"about_ca_system_score_gemma":0.01230929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003953274,"about_ca_topic_score_gemma":0.005660282,"domain_scores_codex":[0.9468392,0.03854334,0.004093303,0.003303279,0.005661874,0.001558954],"domain_scores_gemma":[0.7954249,0.1182063,0.02951883,0.02847175,0.01974238,0.008635803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00226355,0.00115618,0.2070544,0.001020651,0.0002186794,0.0007177302,0.003984205,0.00606564,0.003513633,0.01257367,0.1014941,0.6599376],"study_design_scores_gemma":[0.002099033,0.003023202,0.3722382,0.003730676,0.0006839397,0.00141025,0.004536156,0.08235122,0.01540252,0.0953396,0.4186162,0.0005689773],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2271664,0.004582157,0.5650248,0.07286378,0.002100045,0.02026065,0.01617279,0.01806784,0.07376169],"genre_scores_gemma":[0.4423867,0.001461062,0.5260462,0.00771436,0.001309381,0.009998793,0.006342356,0.0009000381,0.003841003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09190206,"threshold_uncertainty_score":0.4860303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7425196506148799,"score_gpt":0.7031182697319761,"score_spread":0.03940138088290379,"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."}}