{"id":"W4417174767","doi":"10.1088/1361-6579/ae2aa7","title":"An electrical pulse artifact signal for estimating arterial blood pressure: a proof-of-concept study","year":2025,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Artifact (error); Blood pressure; Cuff; SIGNAL (programming language); Carotid arteries; Convolutional neural network; Modality (human–computer interaction); Pulse (music)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004055452,0.0002679533,0.000435241,0.0000692399,0.0000993646,0.00004210288,0.0002918586,0.00009227968,0.00002662403],"category_scores_gemma":[0.0001973398,0.0002212654,0.0001169113,0.0002362765,0.00004163958,0.0001114229,0.00004344066,0.0001921513,0.000001393481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006191206,"about_ca_system_score_gemma":0.00003961234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006280663,"about_ca_topic_score_gemma":0.000001377406,"domain_scores_codex":[0.9981804,0.0001234443,0.0004609619,0.0003978059,0.0004070544,0.0004302735],"domain_scores_gemma":[0.9992551,0.0001139425,0.00006580374,0.0002779191,0.0001886861,0.00009856756],"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":[0.0001177393,0.00127281,0.0004500264,0.000107263,0.0003729329,0.000002286153,0.0001124396,0.03319689,0.9448955,0.00005483786,0.0000590342,0.01935825],"study_design_scores_gemma":[0.001782952,0.002583456,0.005560827,0.0001066776,0.00040328,7.497607e-7,0.00005395965,0.03266705,0.9542494,0.002218105,0.00001593521,0.0003576159],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8721894,0.000456278,0.1237495,0.00001097647,0.0007325498,0.002428974,0.00001373339,0.000308407,0.0001101327],"genre_scores_gemma":[0.9926785,3.725176e-7,0.006258958,0.000008925142,0.0004540326,0.0005704609,0.00000489112,0.0000219269,0.000001966549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.120489,"threshold_uncertainty_score":0.9022941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05044164186516296,"score_gpt":0.2807623402120221,"score_spread":0.2303206983468591,"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."}}