{"id":"W3089154531","doi":"10.2196/21016","title":"Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; University Health Network; University of Toronto; University of Waterloo","funders":"University of Waterloo","keywords":"Data collection; Computer science; Sample (material); mHealth; Data science; Medicine; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.003639973,0.0005917617,0.0006263081,0.002067548,0.004331115,0.001881038,0.001502381,0.0008740216,0.001177163],"category_scores_gemma":[0.009225038,0.0005698387,0.0007560675,0.004549555,0.001808002,0.000793998,0.001675215,0.001138036,0.0002771462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02714717,"about_ca_system_score_gemma":0.05819085,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9742328,"about_ca_topic_score_gemma":0.981716,"domain_scores_codex":[0.996489,0.000746178,0.000171777,0.000336698,0.00120653,0.001049826],"domain_scores_gemma":[0.990426,0.002028794,0.0008928011,0.0003757028,0.004686554,0.001590076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008556505,0.005345425,0.8919886,0.0005373108,0.0001507789,0.002262764,0.04880293,0.001340062,0.003054009,0.001209873,0.002476894,0.04197556],"study_design_scores_gemma":[0.0001411049,0.001320755,0.9038865,0.0001635276,0.0001166968,0.0003550737,0.08073168,0.006105095,0.001077904,0.0001816553,0.005783388,0.0001367328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973858,0.00009907741,0.0003115399,0.0001693009,0.000002802248,0.000421898,0.0005516518,0.00001031191,0.001047623],"genre_scores_gemma":[0.9957116,0.0003593047,0.001771271,0.0002450121,0.000006637661,0.0002762272,0.0006352147,0.00001078213,0.000983984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02714717,"threshold_uncertainty_score":0.1969674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3164965867813092,"score_gpt":0.3661867809386696,"score_spread":0.04969019415736048,"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."}}