{"id":"W6912881020","doi":"10.5683/sp2/tyrrmv","title":"BEAMER: Better Extraction of Activity Metrics from Electronic (medical) Records","year":2019,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Tracking (education); Information extraction; Electronic health record; Data extraction; Extraction (chemistry); Electronic medical record; Health records","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002674411,0.003107764,0.001370823,0.006958868,0.000890181,0.001857201,0.003417065,0.002066047,0.01194478],"category_scores_gemma":[0.009216156,0.00057859,0.002135764,0.004707867,0.0005491485,0.001555345,0.002382116,0.001781873,0.02270743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001956295,"about_ca_system_score_gemma":0.002552739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04018336,"about_ca_topic_score_gemma":0.08598848,"domain_scores_codex":[0.9974154,0.0004862959,0.0004223308,0.0007727225,0.0006029545,0.000300206],"domain_scores_gemma":[0.996515,0.0008390012,0.0003501175,0.001056206,0.0009358302,0.0003038055],"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":[0.0004731521,0.0002970263,0.008885332,0.001742244,0.0002170904,0.0001701992,0.00009554806,0.001699166,0.001698435,0.001035037,0.9423159,0.04137097],"study_design_scores_gemma":[0.0008607792,0.0003479611,0.04826884,0.0008489103,0.0003587543,0.001101239,0.0004708733,0.01445428,0.007411262,0.004679754,0.9209788,0.0002185232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004794201,0.0006422068,0.002509751,0.0004447011,0.0002206018,0.0001505941,0.9853269,0.004020963,0.001890118],"genre_scores_gemma":[0.002775733,0.00009615903,0.003177503,0.00007742956,0.00001953243,0.0001382614,0.9928406,0.00006397703,0.0008108488],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04018336,"threshold_uncertainty_score":0.07989895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0206712130183042,"score_gpt":0.3015939550340966,"score_spread":0.2809227420157924,"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."}}