{"id":"W4285012430","doi":"10.2196/39556","title":"The Use of Automated Machine Translation to Translate Figurative Language in a Clinical Setting: Analysis of a Convenience Sample of Patients Drawn From a Randomized Controlled Trial","year":2022,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Interpreting and Communication in Healthcare","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agency for Healthcare Research and Quality","keywords":"Interpreter; Computer science; Interpretation (philosophy); Artificial intelligence; Asynchronous communication; Natural language processing; Medical education; Medicine","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":[0.03320558,0.001845959,0.004557427,0.00105104,0.000991915,0.00183867,0.001272192,0.00256202,0.005778824],"category_scores_gemma":[0.06261742,0.0008285824,0.003999803,0.001407846,0.002828286,0.00232219,0.0008155898,0.002429085,0.0007242841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426701,"about_ca_system_score_gemma":0.002628535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008392819,"about_ca_topic_score_gemma":0.001120836,"domain_scores_codex":[0.9536245,0.03854308,0.003121231,0.002286886,0.001688967,0.000735354],"domain_scores_gemma":[0.9344732,0.04493522,0.01048442,0.004475086,0.003396135,0.002235829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.9748842,0.005445881,0.002906828,0.001063611,0.00336124,0.00005631509,0.0002565331,0.0002491598,0.0003860325,0.00009850012,0.0003893672,0.01090238],"study_design_scores_gemma":[0.8181334,0.1729247,0.00480119,0.0001163593,0.002022566,0.000042229,0.0001472765,0.0009086191,0.0002378928,0.0002137615,0.0004117045,0.00004025298],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9599491,0.004613975,0.00286999,0.0005770346,0.001030265,0.02875873,0.0007798693,0.0001287562,0.001292346],"genre_scores_gemma":[0.9642298,0.001191615,0.004770527,0.000525603,0.0005026217,0.02769686,0.0004490488,0.00002767247,0.0006063009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03320558,"threshold_uncertainty_score":0.17561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06695911121492158,"score_gpt":0.4908581514232638,"score_spread":0.4238990402083422,"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."}}