{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003661263,0.0004111888,0.0007455103,0.00008085099,0.0006148036,0.00009444682,0.0001203451,0.0001276141,0.000001266009],"category_scores_gemma":[0.0000380893,0.0003797567,0.00004116589,0.0001678137,0.00006703533,0.0001592902,0.000098324,0.0003800914,0.000001293063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001803518,"about_ca_system_score_gemma":0.0004200031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001110397,"about_ca_topic_score_gemma":0.00003247514,"domain_scores_codex":[0.9974065,0.0001127118,0.0006401392,0.0007429724,0.0003003597,0.0007973453],"domain_scores_gemma":[0.9981618,0.0001743366,0.0001474544,0.0001850644,0.0001315772,0.001199779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002834344,0.0002777539,0.6989985,0.06665019,0.0003313224,0.00003441681,0.1851298,0.00006760372,0.000563372,0.00004080473,0.0002479381,0.04737491],"study_design_scores_gemma":[0.02075587,0.0192419,0.5221633,0.001984995,0.001212137,0.000265471,0.400109,0.001267805,0.009341852,0.00001724292,0.0204111,0.003229333],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968782,0.02634927,0.000154584,0.0001528443,0.0003902392,0.003702033,0.0001059963,0.0003269847,0.00003602407],"genre_scores_gemma":[0.995535,0.0005027658,0.002366494,0.00008448144,0.0005550054,0.0008599645,0.00001359959,0.000072345,0.00001039512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2149791,"threshold_uncertainty_score":0.9998654,"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."}}