{"id":"W3131034160","doi":"10.3122/jabfm.2021.s1.200237","title":"On the Front (Phone) Lines: Results of a COVID-19 Hotline","year":2021,"lang":"en","type":"article","venue":"The Journal of the American Board of Family Medicine","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Population Health Research Institute","funders":"","keywords":"Hotline; Medicine; Telehealth; Emergency department; Medicaid; Medical emergency; Health care; Logistic regression; Emergency medicine; Telemedicine; Cohort; Pandemic; Family medicine; Coronavirus disease 2019 (COVID-19); Disease; Internal medicine; Infectious disease (medical specialty); Nursing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007569285,0.0002955908,0.0002698046,0.0003983431,0.001081565,0.0008168311,0.000377962,0.000653832,0.005966027],"category_scores_gemma":[0.003048403,0.0001809938,0.0003925951,0.0004886909,0.000231945,0.0004943122,0.0005262498,0.0007993151,0.0007596174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008017401,"about_ca_system_score_gemma":0.0005422563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01397513,"about_ca_topic_score_gemma":0.02347639,"domain_scores_codex":[0.9992236,0.0001660615,0.00009790643,0.0001293696,0.000166422,0.0002166452],"domain_scores_gemma":[0.9961199,0.0006741044,0.002052187,0.000177772,0.0003279203,0.0006481426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002408261,0.0001861715,0.9912116,0.00004151623,0.00003446494,0.0006345544,0.0005431261,0.00004525507,0.0006081891,0.00001487286,0.001824858,0.004614593],"study_design_scores_gemma":[0.0000103029,0.0002872189,0.9958999,0.00003496567,0.00003059253,0.0006809147,0.001357377,0.0002001063,0.0004708164,0.00001494443,0.001004632,0.000008334851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961463,0.0000964229,0.0001324993,0.0002424587,0.00003165177,0.00007408866,0.001406713,0.00003937045,0.001830439],"genre_scores_gemma":[0.9978532,0.00005609558,0.000252287,0.0004272863,0.00002969359,0.00003143179,0.0005331514,0.00001004463,0.0008067301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01397513,"threshold_uncertainty_score":0.02778763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1149674330747463,"score_gpt":0.4047597796889069,"score_spread":0.2897923466141605,"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."}}