{"id":"W4214952012","doi":"10.20517/ch.2022.01","title":"How a cloud based platform can make ambulatory blood pressure monitoring more efficient, accessible, and evidence based","year":2022,"lang":"en","type":"article","venue":"Connected Health","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"","keywords":"Technician; Cloud computing; Guideline; Computer science; Software deployment; Ambulatory blood pressure; Process (computing); Medicine; Blood pressure; Software engineering; Engineering; Operating system; Pathology; 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":[0.000553531,0.0002670539,0.0007112175,0.0002285523,0.0009380378,0.00006097355,0.0001609106,0.00008365021,0.00004530455],"category_scores_gemma":[0.0002754631,0.0002396434,0.00008308241,0.0004858947,0.00009644166,0.00005261566,0.0001982102,0.0006120194,6.492968e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003603902,"about_ca_system_score_gemma":0.0008350394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000347682,"about_ca_topic_score_gemma":0.00004237213,"domain_scores_codex":[0.9977767,0.0001476453,0.0003205129,0.0005800682,0.0006344058,0.0005405926],"domain_scores_gemma":[0.9982541,0.0003944208,0.0002278555,0.000527686,0.0002195224,0.0003764667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006971021,0.005091771,0.7767953,0.02633906,0.004916637,0.002507414,0.01445471,0.006936767,0.02520137,0.0009433298,0.07371137,0.0561312],"study_design_scores_gemma":[0.03067729,0.00592173,0.6787723,0.007297322,0.01053514,0.0005237957,0.009700956,0.05413181,0.01416681,0.00005075886,0.1859814,0.00224073],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7415469,0.2071827,0.00007108983,0.04819138,0.0009832982,0.00144374,0.0001117965,0.0003879914,0.00008111987],"genre_scores_gemma":[0.9859126,0.0002435546,0.0004463383,0.01245118,0.0004081192,0.0001396237,0.0000204503,0.00004284756,0.0003353129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2443657,"threshold_uncertainty_score":0.9772375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.080472664325999,"score_gpt":0.3178090729246142,"score_spread":0.2373364085986152,"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."}}