{"id":"W3087969094","doi":"10.1109/tbcas.2020.3026992","title":"Wearable Wireless-Enabled Oscillometric Sphygmomanometer: A Flexible Ambulatory Tool for Blood Pressure Estimation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bluetooth; Sphygmomanometer; Wireless; Wearable computer; Computer science; Mobile device; Standard deviation; Biomedical engineering; Computer hardware; Electrical engineering; Embedded system; Engineering; Blood pressure; Medicine; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002248451,0.0005517146,0.0005399187,0.0004479469,0.0001377716,0.0003781835,0.0009803814,0.0005975107,0.001760646],"category_scores_gemma":[0.0005117881,0.0002620606,0.000335973,0.0003475283,0.0001532181,0.0004276849,0.0004305694,0.0002664946,0.0009795635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007755667,"about_ca_system_score_gemma":0.000144959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001199583,"about_ca_topic_score_gemma":0.000185445,"domain_scores_codex":[0.9995444,0.00007324093,0.00003241497,0.0001148099,0.0002104317,0.00002461518],"domain_scores_gemma":[0.9997641,0.00005239225,0.00005836979,0.00004159655,0.00006132856,0.00002210449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004102983,0.0001732605,0.003930458,0.0009823124,0.00009310654,0.0007513682,0.0001531101,0.001489422,0.6855617,0.0007407239,0.003066218,0.3026481],"study_design_scores_gemma":[0.0003894569,0.007231519,0.09043327,0.000356183,0.0007558725,0.01725685,0.0001726816,0.09594024,0.671181,0.001274382,0.1146609,0.0003477386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.194096,0.003320968,0.7897601,0.000382579,0.0006341485,0.0003494961,0.0006566653,0.004577118,0.006223004],"genre_scores_gemma":[0.6980191,0.001769377,0.2896116,0.0005223698,0.0005351145,0.0003546825,0.0004683349,0.0001898455,0.008529506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001760646,"threshold_uncertainty_score":0.005889893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02245886600425598,"score_gpt":0.2232234934003515,"score_spread":0.2007646273960955,"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."}}