{"id":"W2898953128","doi":"10.2196/11177","title":"Content Analysis of Metaphors About Hypertension and Diabetes on Twitter: Exploratory Mixed-Methods Study","year":2018,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute","keywords":"Metaphor; Diabetes mellitus; Disease; Public health; Exploratory research; Object (grammar); Medicine; Psychology; Computer science; Sociology; Pathology; Artificial intelligence; Linguistics; Endocrinology; Social science","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":[],"consensus_categories":[],"category_scores_codex":[0.00109335,0.0002864655,0.000823848,0.0006850447,0.0001137945,0.00003721929,0.0001805058,0.0001376735,0.0004714736],"category_scores_gemma":[0.0001284786,0.000221099,0.0002400069,0.0008509313,0.0002605116,0.0001042909,0.00007915615,0.0001491814,0.00007653537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001945211,"about_ca_system_score_gemma":0.000009395075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003053821,"about_ca_topic_score_gemma":0.00003450711,"domain_scores_codex":[0.9972223,0.0009962367,0.0004879981,0.0006039097,0.0002748582,0.000414706],"domain_scores_gemma":[0.9981923,0.0005240116,0.0002501765,0.0006396276,0.0002654224,0.0001285125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001414866,0.002714315,0.7203814,0.00005515759,0.008449714,0.00001344442,0.0170415,8.057666e-7,0.09873109,0.0002414731,0.002832983,0.1493966],"study_design_scores_gemma":[0.001153294,0.001792097,0.9437885,0.00004157373,0.002664851,2.246295e-7,0.01665145,0.0001291565,0.03271073,0.0001867426,0.0005509498,0.0003304083],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961582,0.001427703,0.00003124063,0.00003784701,0.0006734326,0.0005581272,0.00003163654,0.00007773039,0.001004075],"genre_scores_gemma":[0.9981977,0.00001427165,0.0003108109,0.0007066788,0.000148366,0.0003450901,0.0000315026,0.00003458481,0.0002109415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2234071,"threshold_uncertainty_score":0.9016156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05910732984452725,"score_gpt":0.3559043600682106,"score_spread":0.2967970302236834,"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."}}