{"id":"W3134680231","doi":"10.7759/cureus.13594","title":"Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic","year":2021,"lang":"en","type":"article","venue":"Cureus","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Saskatchewan; University of Ottawa; Canadian Association of Nurses in Oncology; University of British Columbia","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Social media; Medicine; Content analysis; Public health; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Public relations; Medical education; Nursing; Pathology; World Wide Web; Sociology; Political science; Disease; Computer science; Social science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008751251,0.00003866207,0.000122706,0.00003289054,0.0002974711,0.00002607262,0.0001236736,0.0001037678,0.0003634709],"category_scores_gemma":[0.006041666,0.00003220675,0.000036307,0.0005552577,0.000202967,0.00007202291,0.0000292924,0.00008621588,0.000001939028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001586675,"about_ca_system_score_gemma":0.000702644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01197052,"about_ca_topic_score_gemma":0.01055937,"domain_scores_codex":[0.9987631,0.0003217127,0.0001841103,0.0001193014,0.0004814729,0.0001302914],"domain_scores_gemma":[0.998735,0.000718269,0.0001148801,0.0001076789,0.0001132794,0.0002109594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002761078,0.00001780392,0.9556385,0.00001938474,0.00004601607,0.000001340089,0.04072275,1.150851e-7,0.001350323,0.0008388862,0.00000395006,0.001358155],"study_design_scores_gemma":[0.00009964153,0.000004782791,0.9919627,0.000007293766,0.00006625178,6.914979e-7,0.004174597,0.000007598095,0.000112019,0.00008131231,0.00344808,0.00003505782],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869302,0.0003187303,0.0001332274,0.01153472,0.0007851046,0.0001992012,0.000004962494,0.00002079235,0.00007307417],"genre_scores_gemma":[0.9976195,0.001516065,0.00001128374,0.0003562819,0.0003715548,0.00005692864,0.00001140956,0.000003009528,0.00005399046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03654816,"threshold_uncertainty_score":0.9946089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2088737278601454,"score_gpt":0.4339279751268461,"score_spread":0.2250542472667006,"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."}}