{"id":"W4409251308","doi":"10.1055/s-0044-1800716","title":"Primary Care EHR data on Social Determinants of Health: Quality and Fitness for Purpose in Precision/Personalised Medicine","year":2024,"lang":"en","type":"article","venue":"Yearbook of Medical Informatics","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; MacEwan University","funders":"","keywords":"Social determinants of health; Data quality; Health care; Business; Political science; Environmental health; Medicine; Economic growth; Economics; Marketing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003741935,0.0001118482,0.0003890355,0.00005918228,0.00005952294,0.000008046517,0.0003642616,0.000124294,0.0002359781],"category_scores_gemma":[0.0005195977,0.00008995038,0.00002534537,0.0001173385,0.0005135984,0.0002185836,0.000365476,0.0002221441,0.00001004317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001705569,"about_ca_system_score_gemma":0.0001409561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004303566,"about_ca_topic_score_gemma":0.000113302,"domain_scores_codex":[0.9973767,0.0001185956,0.0009779402,0.0001939184,0.001101813,0.0002310706],"domain_scores_gemma":[0.9987404,0.0006256152,0.0002136748,0.0002727366,0.000007543522,0.0001400195],"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":[0.00005098545,0.00006774422,0.004932367,0.003704511,0.000008881667,0.000004165948,0.01792217,0.000005077493,0.00003216965,0.00006760032,0.002346431,0.9708579],"study_design_scores_gemma":[0.01107265,0.001965903,0.787774,0.01534206,0.0001130563,0.00002616584,0.03018609,0.08285984,0.0008295029,0.00241094,0.06627505,0.001144764],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989759,0.000769972,0.002065096,0.00222746,0.0001850136,0.001192991,0.0003406982,0.00002335267,0.003436468],"genre_scores_gemma":[0.9932855,0.000858355,0.002589548,0.00280574,0.0001080098,0.00003883308,0.0002264184,0.00002573387,0.00006189261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9697132,"threshold_uncertainty_score":0.366807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1461469187397929,"score_gpt":0.425879952471461,"score_spread":0.2797330337316682,"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."}}