{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01389012,0.0005678626,0.001144993,0.003250406,0.002614287,0.003158495,0.000972891,0.0009511957,0.003877899],"category_scores_gemma":[0.04338335,0.0006636815,0.001127172,0.003475443,0.001369279,0.00344241,0.002685387,0.00137605,0.000639539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002111425,"about_ca_system_score_gemma":0.002064814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003033963,"about_ca_topic_score_gemma":0.004612704,"domain_scores_codex":[0.9918931,0.005464787,0.0007419783,0.0006968344,0.0006815766,0.0005218229],"domain_scores_gemma":[0.9612904,0.03012679,0.00392774,0.00133163,0.002815532,0.0005080407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.002433458,0.003645416,0.2631624,0.007255114,0.0007083542,0.001285918,0.6331716,0.0004707977,0.003570818,0.003638757,0.005839099,0.07481834],"study_design_scores_gemma":[0.0005975771,0.002459134,0.3375563,0.002246935,0.0007442714,0.0007009585,0.6244964,0.004768744,0.002067482,0.00516781,0.01888606,0.0003083598],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921468,0.0003539769,0.00205408,0.0002821267,0.0000197208,0.002377449,0.001337561,0.00001944839,0.001408813],"genre_scores_gemma":[0.9706511,0.0004745421,0.01056296,0.0006957027,0.00004296562,0.01508773,0.001373437,0.00004209852,0.001069456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01389012,"threshold_uncertainty_score":0.07345885,"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."}}