{"id":"W2167161247","doi":"10.2196/medinform.3204","title":"A Software System to Collect Expert Relevance Ratings of Medical Record Items for Specific Clinical Tasks","year":2014,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Task (project management); Computer science; Relevance (law); Medical record; Matching (statistics); Process (computing); Data science; Information retrieval; Medicine","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":[0.01993428,0.001806356,0.001908083,0.0096032,0.0008874718,0.002070735,0.001418519,0.001581856,0.01259006],"category_scores_gemma":[0.05881776,0.00159656,0.001042885,0.004258004,0.0005523507,0.00241285,0.00221045,0.001352222,0.007300324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360177,"about_ca_system_score_gemma":0.003105024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003102393,"about_ca_topic_score_gemma":0.003957533,"domain_scores_codex":[0.9908465,0.003595082,0.00203843,0.001772333,0.00151332,0.0002342871],"domain_scores_gemma":[0.9095236,0.06432196,0.005154307,0.007558765,0.01121809,0.00222331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004024112,0.003030473,0.04213048,0.001889573,0.000724822,0.0004409242,0.002706847,0.003625832,0.03291785,0.002604055,0.07142925,0.8344757],"study_design_scores_gemma":[0.006941872,0.008302491,0.2510959,0.001054374,0.001778432,0.003132763,0.002204212,0.4903371,0.09256781,0.01550964,0.1254983,0.001577176],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1303639,0.0005861971,0.5988598,0.001304002,0.000453732,0.01918506,0.01522545,0.2201248,0.01389708],"genre_scores_gemma":[0.2195316,0.0002808846,0.7439219,0.0007221455,0.0003432395,0.0150918,0.01233878,0.001888718,0.005880914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01993428,"threshold_uncertainty_score":0.1054238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08452592739631808,"score_gpt":0.4830742366799388,"score_spread":0.3985483092836207,"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."}}