{"id":"W3127976655","doi":"10.1089/pop.2020.0306","title":"Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models","year":2021,"lang":"en","type":"article","venue":"Population Health Management","topic":"Medication Adherence and Compliance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario","funders":"","keywords":"Medicine; Medical prescription; Medication adherence; Retrospective cohort study; Electronic health record; Medication therapy management; Emergency medicine; Population; Cohort; Predictive validity; Prescription drug; Predictive modelling; Polypharmacy; Predictive value; Internal medicine; Family medicine; Health care; Pharmacy; Machine learning; Pharmacist; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04834814,0.001846102,0.001456483,0.001988644,0.0005028896,0.002249754,0.001260045,0.001199637,0.000845761],"category_scores_gemma":[0.07202319,0.0008534961,0.002726515,0.001142054,0.0006049901,0.002256115,0.00156373,0.002720929,0.0003437623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001524731,"about_ca_system_score_gemma":0.001688891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01399618,"about_ca_topic_score_gemma":0.008665219,"domain_scores_codex":[0.9867808,0.01043602,0.0005069274,0.001129962,0.0007063639,0.0004399556],"domain_scores_gemma":[0.8883229,0.1005523,0.00334004,0.003470646,0.003134544,0.001179536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005135037,0.001390491,0.6369842,0.0001691789,0.004178133,0.0001405788,0.0004195861,0.2864468,0.0003831287,0.001386568,0.001372086,0.06199434],"study_design_scores_gemma":[0.0001947687,0.0009921101,0.04795421,0.00007198974,0.0007626076,0.00006096919,0.0001004139,0.9472879,0.0004284177,0.001803883,0.0002854392,0.00005714775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969827,0.001145292,0.02507905,0.001413316,0.0001190608,0.000148211,0.0005906601,0.0003274236,0.001349874],"genre_scores_gemma":[0.9887539,0.0002796564,0.009579419,0.0002046757,0.00009191025,0.00006035507,0.0007106305,0.00002466527,0.0002948025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04834814,"threshold_uncertainty_score":0.2556925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04022153335182458,"score_gpt":0.3323426576397518,"score_spread":0.2921211242879272,"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."}}