{"id":"W4381094908","doi":"10.2196/48297","title":"Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences","keywords":"Interoperability; Computer science; Health care; Data science; Knowledge management; Software engineering; 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":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03368859,0.0003675529,0.001492539,0.0002876848,0.0011632,0.00007691772,0.00132251,0.0007744897,0.0003778153],"category_scores_gemma":[0.00952122,0.0002842347,0.000116692,0.001202772,0.000188945,0.001623971,0.001412856,0.0041692,0.001117126],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023822,"about_ca_system_score_gemma":0.007273375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001274172,"about_ca_topic_score_gemma":0.0005149987,"domain_scores_codex":[0.9838591,0.004031529,0.007420969,0.0003265746,0.002820981,0.001540884],"domain_scores_gemma":[0.9923559,0.001819195,0.002139303,0.001612641,0.000980326,0.001092677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002575858,0.0001307823,0.04683939,0.7094347,0.0001866109,0.00002111216,0.04235783,0.00006596751,5.114524e-7,0.0005431213,0.1121044,0.08805788],"study_design_scores_gemma":[0.001815268,0.0003653022,0.0003088953,0.2429228,0.00004329909,0.0000353094,0.01843218,0.2897286,1.357817e-7,0.00001151607,0.4459028,0.0004339424],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6467966,0.09746534,0.02228846,0.04348469,0.03755812,0.09799919,0.005749444,0.01286204,0.03579612],"genre_scores_gemma":[0.535126,0.3229642,0.003257204,0.09572697,0.01165068,0.006156364,0.02125462,0.0006771025,0.003186933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.466512,"threshold_uncertainty_score":0.999961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2094021149431455,"score_gpt":0.5546051165683983,"score_spread":0.3452030016252527,"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."}}