{"id":"W2175672038","doi":"10.1016/j.compbiomed.2015.11.008","title":"Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model","year":2015,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"National Research Foundation; Neurosciences Research Foundation","keywords":"Gaussian; Confidence interval; Mixture model; Standard deviation; Nonparametric statistics; Mathematics; Blood pressure; Statistics; Cluster analysis; Algorithm; Computer science; Pattern recognition (psychology); Artificial intelligence; Medicine; Chemistry; Internal medicine","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":[0.0007859374,0.0002128696,0.0004061823,0.0005572931,0.00006782518,0.00002512125,0.000446731,0.0002019879,0.000001100479],"category_scores_gemma":[0.0001182103,0.0001440517,0.00001759352,0.00122704,0.0002346029,0.0001837252,0.0001284958,0.0003110316,7.364077e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001269734,"about_ca_system_score_gemma":0.00006543212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002640174,"about_ca_topic_score_gemma":0.000001598854,"domain_scores_codex":[0.998576,0.0001665403,0.0002639117,0.0005209677,0.0001558553,0.0003166916],"domain_scores_gemma":[0.9990114,0.0002264954,0.0001187583,0.0003421633,0.00006414818,0.0002370444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001718091,0.0005735951,0.01008645,0.0001921042,0.000357978,0.0001232446,0.006196208,0.006303709,0.001860612,0.3743383,0.02120145,0.5785945],"study_design_scores_gemma":[0.003306183,0.001153765,0.0006988086,0.0001369588,0.00008084629,0.0001380519,0.00001549132,0.9286225,0.0001240252,0.06490222,0.0005477611,0.0002733377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006642548,0.007133853,0.9828244,0.00185611,0.0003384729,0.0001928837,0.000002476549,0.00007151834,0.0009377716],"genre_scores_gemma":[0.5187163,0.00009061865,0.4803571,0.0007106953,0.00003843589,0.000008026303,0.00001172544,0.000006180405,0.00006092507],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9223188,"threshold_uncertainty_score":0.5874256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02874942015586716,"score_gpt":0.3150704457048422,"score_spread":0.286321025548975,"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."}}