{"id":"W2954479257","doi":"10.2196/14107","title":"Common Data Elements for Acute Coronary Syndrome: Analysis Based on the Unified Medical Language System","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Standardization; Documentation; Unified Medical Language System; Computer science; Information retrieval; Annotation; Natural language processing; Artificial intelligence; Programming language","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008618036,0.0002859968,0.0009436863,0.0002531104,0.0005283316,0.00002088136,0.002170943,0.0007227526,0.00309987],"category_scores_gemma":[0.0006887849,0.0001752044,0.0001511532,0.0008121565,0.00008931213,0.0001929879,0.0004844686,0.001804576,0.001332503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005573861,"about_ca_system_score_gemma":0.002610852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001593322,"about_ca_topic_score_gemma":0.0003347306,"domain_scores_codex":[0.9925322,0.0009527619,0.002425487,0.0002706868,0.002687254,0.001131637],"domain_scores_gemma":[0.9914176,0.004567971,0.0008484116,0.002266983,0.0001282043,0.0007707578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00178465,0.00150182,0.24704,0.03969619,0.01097144,0.0005416757,0.03162948,0.00009090923,0.000008263052,0.03594638,0.5989925,0.03179667],"study_design_scores_gemma":[0.002992107,0.0004873784,0.004083453,0.001752199,0.0004231841,0.00003162622,0.01522658,0.9411647,0.000001027483,0.00001013264,0.03355382,0.0002738374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565542,0.00006946649,0.007916299,0.01097026,0.00207279,0.01019562,0.0009843779,0.0004730453,0.01076393],"genre_scores_gemma":[0.9761766,0.00001975631,0.0004782165,0.01855625,0.000186246,0.001017207,0.002477268,0.00004363433,0.001044883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9410737,"threshold_uncertainty_score":0.9994451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05425353853714412,"score_gpt":0.4539161491275243,"score_spread":0.3996626105903802,"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."}}