{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006062654,0.000683921,0.000729946,0.000596861,0.0007331735,0.0006225039,0.0004557975,0.0009147052,0.002924988],"category_scores_gemma":[0.005440738,0.0005520791,0.0008256843,0.0003982896,0.001177143,0.0007366093,0.000689258,0.0006872772,0.0004736164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006332573,"about_ca_system_score_gemma":0.0009838631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008383041,"about_ca_topic_score_gemma":0.0009397267,"domain_scores_codex":[0.995684,0.002698143,0.0002399858,0.0004403423,0.0005664461,0.0003710345],"domain_scores_gemma":[0.9953405,0.001875571,0.001122571,0.0004567736,0.0005315561,0.0006730828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"nonrandomized_trial","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.1183142,0.3966175,0.3669436,0.0009107718,0.0004108192,0.0004123012,0.009710354,0.0007108799,0.01465927,0.0002735023,0.0007073114,0.09032942],"study_design_scores_gemma":[0.010304,0.6385943,0.3447689,0.00003564173,0.0001774883,0.0001666428,0.002009715,0.001080769,0.002152397,0.00006976787,0.0005833211,0.00005704528],"study_design_candidate":"nonrandomized_trial","study_design_consensus":"nonrandomized_trial","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99855,0.00001503147,0.0002296709,0.000009502869,0.000003554351,0.0009756367,0.00004029772,0.000003483543,0.0001728416],"genre_scores_gemma":[0.9953307,0.00004238841,0.001357964,0.00007180358,0.00001541067,0.002803975,0.00007375641,0.0000021597,0.0003019356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006062654,"threshold_uncertainty_score":0.03206277,"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."}}