{"id":"W4402282744","doi":"10.2196/57608","title":"Implementation and User Satisfaction of a Comprehensive Telemedicine Approach for SARS-CoV-2 Self-Sampling: Monocentric, Prospective, Interventional, Open-Label, Controlled, Two-Arm Feasibility Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Technische Universität München; Bundesministerium für Bildung und Forschung","keywords":"Telemedicine; Preprint; Coronavirus disease 2019 (COVID-19); Medicine; Sampling (signal processing); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Medical emergency; Emergency medicine; Computer science; Internal medicine; World Wide Web; Health care; Virology; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002315851,0.0002321284,0.0006630325,0.0006541979,0.0003114108,0.0001865416,0.0001371625,0.00007031813,0.00002088752],"category_scores_gemma":[0.0002379603,0.000183284,0.0001037538,0.0009320511,0.0001609464,0.0005889692,0.0002485674,0.0005418574,0.000006485861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005026721,"about_ca_system_score_gemma":0.0002148943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004232172,"about_ca_topic_score_gemma":0.00009624568,"domain_scores_codex":[0.9970426,0.0003786256,0.0008383698,0.0005501586,0.00077185,0.0004184009],"domain_scores_gemma":[0.9971016,0.0008946449,0.000200497,0.0002569564,0.001480006,0.00006627349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.02942081,0.02727052,0.3571765,0.01099864,0.006145386,0.00004505318,0.04015428,0.000007643937,0.4533025,0.007274091,0.006924073,0.06128047],"study_design_scores_gemma":[0.2199061,0.04268319,0.3551005,0.001189251,0.0007603276,0.0003055272,0.09713255,0.08591089,0.190939,0.00305189,0.002156566,0.0008641726],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758285,0.0003796832,0.007813347,0.0001195388,0.0001305805,0.01498022,0.00005374395,0.0001172385,0.0005771278],"genre_scores_gemma":[0.9937279,0.00001322647,0.003620632,0.00006123574,0.00009715371,0.00237547,0.00004421038,0.00003374596,0.00002647539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2623635,"threshold_uncertainty_score":0.7474105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2689628294869456,"score_gpt":0.5327349872426125,"score_spread":0.2637721577556669,"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."}}