{"id":"W3035916362","doi":"10.2196/19866","title":"The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Telemedicine; Health care; Medicine; Public health; Telehealth; Coronavirus disease 2019 (COVID-19); Middle East respiratory syndrome; Digital health; Global health; Health informatics; Medical emergency; Business; Disease; Economic growth; Nursing; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001838328,0.0001517304,0.0004272735,0.00009431294,0.0002865068,0.00004251957,0.0003518937,0.0001871089,0.0000249904],"category_scores_gemma":[0.008788922,0.00009209327,0.00003470657,0.001069298,0.0004832593,0.0002387505,0.0003274899,0.0005817592,0.000005394623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002454252,"about_ca_system_score_gemma":0.003172158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005980206,"about_ca_topic_score_gemma":0.0006703217,"domain_scores_codex":[0.9972723,0.0001340829,0.001548434,0.0001069739,0.0005500778,0.0003881128],"domain_scores_gemma":[0.9962587,0.001146427,0.00136187,0.0003968429,0.00008680202,0.0007493488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003739995,0.0001374928,0.6700038,0.000826003,0.00007962176,7.882616e-7,0.04921738,0.00000289233,0.000002305155,0.007073464,0.02452814,0.2480908],"study_design_scores_gemma":[0.001604374,0.0003974222,0.06065296,0.0001495914,0.00002563128,0.0001028102,0.04354992,0.01338143,0.000001110269,0.003318487,0.876684,0.0001323063],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2392131,0.00384897,0.001112542,0.7546793,0.00003828499,0.000878224,0.00005905716,0.0000939172,0.00007652809],"genre_scores_gemma":[0.7693472,0.01238645,0.001490709,0.2164979,0.00006505313,0.0001449622,0.0000545203,0.00001110422,0.000002074538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8521558,"threshold_uncertainty_score":0.9995605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.089719897220137,"score_gpt":0.4288465134815742,"score_spread":0.3391266162614371,"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."}}