{"id":"W3095091779","doi":"10.2196/22031","title":"The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; U.S. National Library of Medicine","keywords":"Generalizability theory; Software portability; Computer science; Audit; Documentation; Health informatics; Medical record; Electronic prescribing; Data mining; Medicine; Statistics; Public health; Family medicine; Pharmacy; 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":[0.1701035,0.0005782736,0.0008231524,0.004818489,0.001082804,0.002683994,0.001674927,0.001053765,0.002542748],"category_scores_gemma":[0.4440643,0.0004453479,0.002281854,0.00314047,0.003417131,0.002942953,0.00312052,0.001035876,0.0004539034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003101908,"about_ca_system_score_gemma":0.001816122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001892269,"about_ca_topic_score_gemma":0.00151131,"domain_scores_codex":[0.8272036,0.1072004,0.01613586,0.01276529,0.0351484,0.001546398],"domain_scores_gemma":[0.3961363,0.4913983,0.03486516,0.02906109,0.04687235,0.001666778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002352571,0.001170208,0.8881974,0.0009763488,0.002101218,0.000131818,0.01683767,0.003082387,0.002556771,0.002021018,0.001419305,0.07915331],"study_design_scores_gemma":[0.0002696481,0.007922169,0.9510385,0.0003537331,0.0007898846,0.0002849388,0.0107586,0.01790465,0.004594559,0.002275087,0.003678628,0.0001295515],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658126,0.0002826007,0.02486907,0.0002187074,0.00008470254,0.002707395,0.0007897333,0.0001990139,0.005036072],"genre_scores_gemma":[0.9891983,0.00004098374,0.008749041,0.00007801534,0.00002690955,0.001266875,0.0003730353,0.00004927476,0.0002177067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1701035,"threshold_uncertainty_score":0.899604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1049781198490898,"score_gpt":0.4842708153934331,"score_spread":0.3792926955443433,"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."}}