{"id":"W2588770144","doi":"10.1109/tim.2017.2657978","title":"Metrological Characterization of a Method for Blood Pressure Estimation Based on Arterial Lumen Area Model","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Blood pressure; Standard deviation; Waveform; Diastole; Amplitude; Medicine; Biomedical engineering; Algorithm; Mathematics; Cardiology; Computer science; Internal medicine; Statistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003240503,0.0001458168,0.0003220259,0.0001193117,0.0003605084,0.00003845594,0.00004773932,0.00008725264,0.00002891197],"category_scores_gemma":[0.00002451044,0.0001200338,0.0000924738,0.0000302493,0.0000487415,0.0001151607,0.000001075114,0.00008420674,8.096047e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001142957,"about_ca_system_score_gemma":0.00005126825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001041941,"about_ca_topic_score_gemma":0.000006332783,"domain_scores_codex":[0.9989083,0.00004035243,0.0002720669,0.0002488837,0.000412425,0.0001179301],"domain_scores_gemma":[0.9991991,0.00003189775,0.0001961993,0.0002499732,0.0002532344,0.00006959918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004435817,0.001691438,0.000090143,0.0003393883,0.001271159,0.000001379634,0.000394981,0.01180806,0.8594512,0.0001453561,0.00009699426,0.1202741],"study_design_scores_gemma":[0.008647634,0.00126541,0.004461549,0.0001912085,0.004483322,0.000005468693,0.00004505461,0.5965745,0.3837007,0.00008360594,0.0003780627,0.0001635329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05206001,0.00002006253,0.94471,0.001578812,0.0002604407,0.0009728268,0.0001061792,0.00003533219,0.0002562927],"genre_scores_gemma":[0.9795721,0.00002575614,0.01920251,0.0008258664,0.00002850464,0.000231304,0.00002041419,0.00001207223,0.0000815137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.927512,"threshold_uncertainty_score":0.4894835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09344157842562725,"score_gpt":0.3189332510522804,"score_spread":0.2254916726266532,"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."}}