{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002385585,0.00083571,0.0003226988,0.0006400662,0.0002238571,0.001572241,0.001382097,0.00078775,0.004045979],"category_scores_gemma":[0.005980205,0.0003716153,0.0005991245,0.0003869844,0.0005864878,0.001636565,0.001885558,0.0007478009,0.0007268753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000263744,"about_ca_system_score_gemma":0.0004082268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004779065,"about_ca_topic_score_gemma":0.0005742307,"domain_scores_codex":[0.9982415,0.0007661059,0.0001135198,0.000253034,0.0005242376,0.0001015445],"domain_scores_gemma":[0.9974104,0.001436611,0.000241902,0.0003653244,0.0003895948,0.0001562655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001103354,0.0005894227,0.01386555,0.002326339,0.0001840977,0.001686446,0.004446624,0.005782924,0.1525577,0.008529635,0.022833,0.786095],"study_design_scores_gemma":[0.0008943868,0.007013231,0.06813008,0.002203337,0.0006381649,0.01766407,0.003223602,0.1328225,0.2533535,0.017934,0.4953402,0.0007828746],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1306369,0.003360463,0.8310286,0.002321837,0.000555482,0.0009909595,0.0004018413,0.01827635,0.01242753],"genre_scores_gemma":[0.4378088,0.002149495,0.5479079,0.001508405,0.0003534391,0.0006170598,0.0004871379,0.001092334,0.008075385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004045979,"threshold_uncertainty_score":0.0135352,"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."}}