{"id":"W4309702693","doi":"10.3389/fcvm.2022.1048507","title":"Enhancing central blood pressure accuracy through statistical modeling: A proof-of-concept study","year":2022,"lang":"en","type":"article","venue":"Frontiers in Cardiovascular Medicine","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Hôpital du Sacré-Cœur de Montréal; Université de Montréal","funders":"","keywords":"Medicine; Blood pressure; Cardiology; Cuff; Internal medicine; Diastole; Calibration; Hemodynamics; Population; Linear regression; Regression analysis; Statistics; Surgery; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001396322,0.0002809078,0.001428899,0.0001982191,0.0001362238,0.000005475132,0.0002526551,0.00008032614,0.0001158207],"category_scores_gemma":[0.0004482165,0.0002459185,0.00033094,0.0004980193,0.0001396834,0.0000760578,0.00011347,0.0007643798,2.577403e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001243325,"about_ca_system_score_gemma":0.000235347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006837677,"about_ca_topic_score_gemma":0.000002894084,"domain_scores_codex":[0.9961433,0.0004855273,0.0007143505,0.0006153813,0.001491759,0.0005496671],"domain_scores_gemma":[0.9986184,0.00009105038,0.000100554,0.0009229811,0.00009821908,0.0001687934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003236043,0.008996317,0.167739,0.001951196,0.05394205,0.006054231,0.1164426,0.4223222,0.0009995353,0.0003976636,0.005472265,0.212447],"study_design_scores_gemma":[0.1414391,0.02337393,0.02409102,0.002863262,0.0521634,0.001874763,0.1782461,0.4447162,0.01395785,0.004680137,0.1091279,0.003466317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2266976,0.1448118,0.6197065,0.0003675024,0.004404982,0.003199658,0.00005184926,0.0001195304,0.0006406669],"genre_scores_gemma":[0.9916757,0.00008052609,0.007082745,0.00007474315,0.0006507701,0.0001867879,0.00005730522,0.0000551567,0.0001362255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7649781,"threshold_uncertainty_score":0.9999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183933775068951,"score_gpt":0.2793684974342764,"score_spread":0.2609751199273813,"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."}}