{"id":"W3024871347","doi":"10.2196/15623","title":"Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences","keywords":"Reproductive health; Social media; Social media analytics; Systemic lupus erythematosus; Misinformation; Population; Descriptive statistics; Medicine; Analytics; Public health; Health care; Internet privacy; Computer science; Disease; Data science; World Wide Web; Nursing; Environmental health; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03561619,0.002367612,0.003549299,0.004841015,0.006316041,0.003709041,0.002185957,0.003661257,0.09111422],"category_scores_gemma":[0.0725913,0.00258801,0.003624186,0.004219022,0.002390567,0.004278178,0.004756195,0.005777066,0.02457916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006188308,"about_ca_system_score_gemma":0.022971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00587255,"about_ca_topic_score_gemma":0.01082433,"domain_scores_codex":[0.9847564,0.007266686,0.003515991,0.001338059,0.001888584,0.001234282],"domain_scores_gemma":[0.9388708,0.02077319,0.005130119,0.007784492,0.02474243,0.00269893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.02674403,0.01247675,0.01161066,0.0727545,0.0006292586,0.002328327,0.03616755,0.004918168,0.0102013,0.01558709,0.4007877,0.4057946],"study_design_scores_gemma":[0.02663548,0.007054127,0.03693175,0.03155626,0.0006110401,0.0005306642,0.03411582,0.004579284,0.008135381,0.01831368,0.8305396,0.000996857],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.002729147,0.0001292955,0.0031682,0.0004857699,0.0002669177,0.9817538,0.008962908,0.0001755462,0.002328408],"genre_scores_gemma":[0.0007600438,0.00006268421,0.002654081,0.0001325718,0.00001870792,0.9954345,0.0005647549,0.000009545489,0.0003630596],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.09111422,"threshold_uncertainty_score":0.3048074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7812413687235711,"score_gpt":0.6567425681264537,"score_spread":0.1244988005971174,"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."}}