{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04869695,0.0002133861,0.0005404988,0.0001405577,0.0002951358,0.000006485358,0.001075324,0.0003941514,0.001004967],"category_scores_gemma":[0.01873143,0.0001886869,0.00004630643,0.0003725204,0.00009160093,0.0002961132,0.0003452029,0.001413848,0.0001707304],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009140391,"about_ca_system_score_gemma":0.007436346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001084882,"about_ca_topic_score_gemma":0.009221849,"domain_scores_codex":[0.9867416,0.007103824,0.001451178,0.000697894,0.002351447,0.001654041],"domain_scores_gemma":[0.9938706,0.002143814,0.0007860241,0.001892025,0.0007450148,0.0005625575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001477408,0.0002021204,0.01006495,0.0005901468,0.0001575118,0.000001411424,0.0004243862,0.000001107921,0.00002142086,0.006410762,0.03406628,0.9479122],"study_design_scores_gemma":[0.009667339,0.0005863131,0.1678735,0.0006751537,0.0006712151,0.00001722895,0.0008975407,0.07467687,0.00009415879,0.006538701,0.7374597,0.0008422788],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8008692,0.003770143,0.008398843,0.02326617,0.008103484,0.02800935,0.0006142072,0.0006684729,0.1263002],"genre_scores_gemma":[0.9908234,0.0001843216,0.0001487785,0.0008601588,0.00120702,0.00532739,0.0005765241,0.00004442261,0.0008279369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9470699,"threshold_uncertainty_score":0.9999083,"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."}}