{"id":"W2258148286","doi":"","title":"Evaluation of Electronic Medical Record Administrative data Linked Database (EMRALD).","year":2014,"lang":"en","type":"article","venue":"PubMed","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Medical prescription; Medical record; Electronic medical record; Health care; Family medicine; Medical emergency; Electronic health record; Electronic database; Primary care; Health records; Database; Emergency medicine; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.132757,0.0009098025,0.001208046,0.005343859,0.0007872701,0.004454081,0.002975197,0.001054793,0.002088484],"category_scores_gemma":[0.2738736,0.0006098258,0.001688669,0.008461051,0.000516278,0.0027891,0.002996684,0.0007150382,0.0007367472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006392747,"about_ca_system_score_gemma":0.007401994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07201688,"about_ca_topic_score_gemma":0.05197451,"domain_scores_codex":[0.8889177,0.05970835,0.0141999,0.005163889,0.03061757,0.001392592],"domain_scores_gemma":[0.6836219,0.1594466,0.0398403,0.02181243,0.08922868,0.00605001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01363706,0.001002747,0.8148143,0.004042122,0.003056326,0.0001233461,0.001012966,0.001913442,0.000660886,0.0009254079,0.01442664,0.1443848],"study_design_scores_gemma":[0.001816614,0.002373454,0.95214,0.001366833,0.001502853,0.0003171282,0.0008326364,0.01844118,0.00121885,0.0002224948,0.0196718,0.00009599984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.741965,0.009690613,0.01947623,0.003454616,0.000564178,0.02070764,0.185665,0.002230555,0.01624613],"genre_scores_gemma":[0.8610971,0.001518559,0.03385361,0.001152689,0.0001777168,0.006078755,0.09475299,0.0001582534,0.001210459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.132757,"threshold_uncertainty_score":0.7020947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3579404250033273,"score_gpt":0.505255538148337,"score_spread":0.1473151131450097,"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."}}