{"id":"W4405248811","doi":"10.1177/20552076241300748","title":"RemoteHealthConnect: Innovating patient monitoring with wearable technology and custom visualization","year":2024,"lang":"en","type":"article","venue":"Digital Health","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Sheridan College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Visualization; Computer science; Human–computer interaction; Wearable technology; Computer graphics (images); Engineering; Embedded system; Artificial intelligence","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":[0.0001343588,0.00009747863,0.0001189616,0.000389745,0.000147332,0.0006123099,0.000141923,0.00003840454,8.143836e-7],"category_scores_gemma":[0.00003733941,0.00008298834,0.000008364822,0.002074271,0.00003172018,0.0009242896,0.0001196962,0.0001025994,0.00001450549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007123432,"about_ca_system_score_gemma":0.0002165602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001622276,"about_ca_topic_score_gemma":0.000002159674,"domain_scores_codex":[0.998991,0.00001419872,0.0002468164,0.0003195803,0.000180019,0.0002484151],"domain_scores_gemma":[0.9995286,0.00003210238,0.00007071753,0.0001994252,0.0000779266,0.00009118269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001492483,0.0000234876,0.002803566,0.0001883567,0.000006155359,0.00001037476,0.0005666682,0.00001934989,0.000005245193,0.2167968,0.0001343945,0.7794441],"study_design_scores_gemma":[0.0007113253,0.002363136,0.001321466,0.00568212,0.00000698133,0.0003409644,0.001753885,0.8734406,0.001007102,0.01788003,0.09456249,0.0009298607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08522966,0.003094972,0.9035028,0.004807182,0.0003785955,0.00025609,0.0000140961,0.001052818,0.001663839],"genre_scores_gemma":[0.9941972,0.000166163,0.005113379,0.000339668,0.00003733092,0.000003306974,0.000009978178,0.00001419792,0.0001187026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9089676,"threshold_uncertainty_score":0.5904518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726637639165093,"score_gpt":0.3138530274305928,"score_spread":0.2965866510389419,"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."}}