{"id":"W1990598610","doi":"10.2196/ijmr.2457","title":"Correction: Improving Interoperability in ePrescribing","year":2012,"lang":"en","type":"erratum","venue":"Interactive Journal of Medical Research","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical prescription; Interoperability; Table (database); Sample (material); Computer science; Set (abstract data type); Statistics; Minor (academic); Information retrieval; Medicine; Data mining; Mathematics; World Wide Web; Political science; Programming language; 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":[0.02114028,0.003305629,0.002362545,0.0058197,0.004075464,0.006004947,0.005271928,0.009767005,0.03908198],"category_scores_gemma":[0.2425371,0.001986797,0.003239998,0.006523707,0.003149163,0.004416313,0.003415067,0.01469928,0.02828253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005815737,"about_ca_system_score_gemma":0.01168223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02338463,"about_ca_topic_score_gemma":0.01979811,"domain_scores_codex":[0.9676594,0.005532885,0.009212491,0.0024637,0.01343678,0.001694787],"domain_scores_gemma":[0.7875606,0.06288441,0.009304103,0.01417982,0.1224026,0.003668428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005906365,0.00001856081,0.0001215649,0.0002986179,0.00002442536,0.0001811358,0.00008921867,0.0000522231,0.0000689981,0.0008729316,0.9884098,0.009803484],"study_design_scores_gemma":[0.00007643166,0.0000556119,0.00122146,0.000968991,0.00008159225,0.0004525186,0.0002343088,0.0004318055,0.0007219809,0.001378322,0.9943048,0.00007210998],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0002866988,0.001071055,0.001908654,0.1014166,0.8901647,0.00007057837,0.00179722,0.0009719747,0.002312463],"genre_scores_gemma":[0.02602366,0.01461705,0.03137944,0.4250093,0.3111372,0.0009279737,0.007298719,0.004631972,0.1789747],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03908198,"threshold_uncertainty_score":0.1307423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2540911673788983,"score_gpt":0.5582555845654558,"score_spread":0.3041644171865575,"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."}}