{"id":"W2136435586","doi":"10.1111/cts.12233","title":"Mixing Politics and Medicine: A Case Study","year":2014,"lang":"en","type":"editorial","venue":"Clinical and Translational Science","topic":"Health and Conflict Studies","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Politics; Globe; Harm; Reductionism; Vetting; Political science; Alternative medicine; Library science; Medicine; Law; Epistemology; Computer science; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.009740385,0.001285446,0.001059334,0.003920286,0.02105441,0.008833111,0.002814448,0.0139451,0.005841126],"category_scores_gemma":[0.03144207,0.001027099,0.001373278,0.004488787,0.00975288,0.007060444,0.008902417,0.01051643,0.001297632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008143151,"about_ca_system_score_gemma":0.004594219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00589332,"about_ca_topic_score_gemma":0.01619828,"domain_scores_codex":[0.9845558,0.01067221,0.0006889867,0.0007766472,0.001442546,0.001863887],"domain_scores_gemma":[0.9792874,0.012772,0.002546082,0.000689531,0.0007668333,0.003938077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001083141,0.0008549409,0.01511529,0.0005487769,0.00004403173,0.8009846,0.1205025,0.0002953286,0.0004644515,0.03224866,0.01689491,0.01193814],"study_design_scores_gemma":[0.0000860059,0.0003040871,0.005726896,0.002094607,0.00006813418,0.6501187,0.194463,0.0008699452,0.000553281,0.01141415,0.134181,0.0001201315],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"editorial","genre_scores_codex":[0.7047997,0.03050097,0.008016082,0.1427805,0.00403752,0.001406951,0.0003090419,0.0001322876,0.1080168],"genre_scores_gemma":[0.9271978,0.01721669,0.006922473,0.03117762,0.003732338,0.0005585953,0.0001270008,0.0001168855,0.0129506],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.02105441,"threshold_uncertainty_score":0.05908298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1953329916126175,"score_gpt":0.5849099503090293,"score_spread":0.3895769586964118,"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."}}