{"id":"W3111335965","doi":"10.1007/978-3-030-57566-3_3","title":"Wearable Technology for Presumptive Diagnosis of High Blood Pressure Based on Risk Factors","year":2020,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Healthcare Systems and Public Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wearable computer; Blood pressure; Risk analysis (engineering); Medicine; Computer science; Business; Internal medicine; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003220273,0.0006621403,0.0003295631,0.0008233081,0.0001868383,0.0009599888,0.0007078659,0.001029479,0.01365327],"category_scores_gemma":[0.000775504,0.0002060278,0.000399476,0.0006320074,0.0003935004,0.0009770143,0.0006221668,0.0009656598,0.008315743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001712014,"about_ca_system_score_gemma":0.0002518861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003581303,"about_ca_topic_score_gemma":0.0007590624,"domain_scores_codex":[0.9998192,0.00003352233,0.00001028823,0.00002966908,0.00009535155,0.00001199937],"domain_scores_gemma":[0.9997193,0.0002014414,0.00001001538,0.00001795895,0.00004119667,0.00001000051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006964025,0.00007523647,0.001414515,0.0006269422,0.00002587971,0.00101348,0.0003672614,0.0003988163,0.01481886,0.02139884,0.08399362,0.8757969],"study_design_scores_gemma":[0.00003173849,0.0003080608,0.006463177,0.001839271,0.0001126447,0.01720256,0.00048848,0.007293238,0.02039813,0.04209678,0.9036646,0.0001012618],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01285103,0.1852775,0.3276602,0.007145923,0.01088693,0.0002845257,0.001006014,0.002003292,0.4528847],"genre_scores_gemma":[0.09131349,0.1335118,0.1509397,0.007535996,0.004540799,0.0003457892,0.001005279,0.0003546787,0.6104525],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01365327,"threshold_uncertainty_score":0.04567474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03742374193005601,"score_gpt":0.2803351701433559,"score_spread":0.2429114282132999,"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."}}