{"id":"W3021016914","doi":"10.2196/18012","title":"Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fogarty International Center; National Institute on Aging","keywords":"Usability; Blood pressure; Wearable computer; mHealth; Wearable technology; Medicine; Computer science; Multilayer perceptron; Photoplethysmogram; Health care; Artificial intelligence; Artificial neural network; Wireless; Human–computer interaction; Internal medicine; Embedded system; Nursing; Telecommunications","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":[0.003198504,0.0005059447,0.0003692283,0.0005315712,0.000200122,0.0005256507,0.0005698579,0.0005366842,0.001796508],"category_scores_gemma":[0.006734529,0.0001738167,0.000360875,0.0002472308,0.0002103984,0.0006815792,0.0005563725,0.0002853441,0.0004554019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002278497,"about_ca_system_score_gemma":0.0003595642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007246027,"about_ca_topic_score_gemma":0.0008488221,"domain_scores_codex":[0.998602,0.0006697632,0.0001228137,0.0001398167,0.0003705524,0.00009500668],"domain_scores_gemma":[0.9972528,0.001183083,0.0001271175,0.0001736858,0.001137557,0.0001257878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002698783,0.005968906,0.1185796,0.004685006,0.0003841372,0.003358818,0.008112963,0.002492533,0.132999,0.001323548,0.009935951,0.7094606],"study_design_scores_gemma":[0.001599462,0.07762249,0.5992002,0.002340706,0.002144224,0.01485119,0.01066195,0.07307687,0.1188075,0.000680029,0.09855682,0.0004585444],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722225,0.0007678655,0.0215357,0.000330528,0.00006618608,0.002406113,0.0002493745,0.0005466637,0.001874964],"genre_scores_gemma":[0.9172303,0.0009205852,0.0763812,0.0002903465,0.00004869635,0.001801915,0.000655357,0.00008480921,0.002586768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003198504,"threshold_uncertainty_score":0.0169155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03468697295502949,"score_gpt":0.304018751156686,"score_spread":0.2693317782016565,"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."}}