{"id":"W3103186681","doi":"10.2196/23353","title":"Universal Patient Identifier and Interoperability for Detection of Serious Drug Interactions: Retrospective Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pharmacy; Identifier; Medicine; Health information exchange; Health care; Interoperability; Population; Medical prescription; Medical emergency; Computer security; Business; Internet privacy; Family medicine; Computer science; Nursing; Environmental health; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008769315,0.0001242963,0.0003791758,0.00006909194,0.0002621685,0.000007799142,0.0001340769,0.0001072584,0.0001218888],"category_scores_gemma":[0.0009326599,0.0001022747,0.00004874767,0.0002041051,0.00007543916,0.0002968063,0.000149026,0.0008534148,0.00002688932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000425472,"about_ca_system_score_gemma":0.0003998229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002781144,"about_ca_topic_score_gemma":0.0008970732,"domain_scores_codex":[0.9974482,0.0003528756,0.001309324,0.0001377618,0.0004353947,0.0003164419],"domain_scores_gemma":[0.9982946,0.0003572185,0.0004929557,0.0001861834,0.0003300918,0.0003389729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.001007451,0.0006389433,0.0934042,0.006398143,0.0002251864,0.000003801775,0.8064788,0.00000215966,0.0002508772,0.0002753081,0.007216775,0.08409834],"study_design_scores_gemma":[0.008728575,0.006361502,0.04901463,0.001256504,0.0001108336,0.00001511091,0.8216789,0.05167284,0.000461083,0.0002951933,0.0598496,0.000555207],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909201,0.00002118274,0.003151799,0.000846906,0.0007302365,0.003801783,0.00002063513,0.00007511634,0.0004322149],"genre_scores_gemma":[0.9983538,0.00001303055,0.0001253526,0.000864108,0.0001428144,0.0004053334,0.000006066442,0.00001311093,0.0000763253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08354313,"threshold_uncertainty_score":0.4170642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03372243450602033,"score_gpt":0.4057751859100328,"score_spread":0.3720527514040124,"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."}}