{"id":"W4413226953","doi":"10.2196/69140","title":"Monitoring People With COVID-19 at Home With the COVIDFree@Home Program: Feasibility Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Public Health Ontario; William Osler Health System; Trillium Health Centre; Institute for Clinical Evaluative Sciences; University Health Network; Toronto Metropolitan University; SickKids Foundation; University of Toronto; Hospital for Sick Children; Vector Institute; Sunnybrook Health Science Centre","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Cohort; Gerontology; Medicine; Virology; Computer science; World Wide Web; Outbreak; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004472206,0.0007209651,0.0006200646,0.0009136269,0.002208961,0.001137774,0.0009893139,0.001116238,0.002893077],"category_scores_gemma":[0.005970284,0.0009945907,0.001286475,0.0007711268,0.0007526876,0.002116263,0.001812599,0.001629486,0.001539742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00114954,"about_ca_system_score_gemma":0.00263155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01383878,"about_ca_topic_score_gemma":0.02210646,"domain_scores_codex":[0.9976579,0.000913894,0.0001888051,0.0003461732,0.0003636804,0.0005294264],"domain_scores_gemma":[0.9962528,0.0005967229,0.0008664231,0.0004218216,0.000796972,0.001065315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003548735,0.01054436,0.9705036,0.00019024,0.0001665179,0.0005353414,0.004633502,0.00007439128,0.0008304878,0.0001208744,0.00176684,0.007085115],"study_design_scores_gemma":[0.001595525,0.02128871,0.9608629,0.0001447186,0.000167679,0.0006792552,0.0117357,0.0006104855,0.0003284275,0.0001131973,0.002397165,0.00007614995],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938975,0.00005218829,0.0003790141,0.0001384971,0.00002052303,0.003874631,0.001006456,0.000009049671,0.0006221552],"genre_scores_gemma":[0.9882622,0.0001501466,0.001518977,0.0006225337,0.0000485169,0.007607571,0.001037843,0.00001069793,0.0007414464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01383878,"threshold_uncertainty_score":0.02751642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1366490522614455,"score_gpt":0.5686704077334751,"score_spread":0.4320213554720296,"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."}}