{"id":"W4402263463","doi":"10.2196/51435","title":"ChatGPT May Improve Access to Language-Concordant Care for Patients With Non–English Language Preferences","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Mandarin Chinese; Preference; Linguistics; Psychology; Computer science; Natural language processing; World Wide Web","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":[0.0001554978,0.0001448872,0.0001922941,0.0001670934,0.00007932398,0.0001032997,0.0001670798,0.0001423949,0.0004470799],"category_scores_gemma":[0.0009029018,0.0001035715,0.00005084068,0.0003310989,0.00004509372,0.0002126787,0.00002839011,0.0002267798,0.00006332596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002001625,"about_ca_system_score_gemma":0.002543314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008016765,"about_ca_topic_score_gemma":0.0004360682,"domain_scores_codex":[0.998562,0.0000219026,0.0003009384,0.0003804901,0.0004610819,0.0002735462],"domain_scores_gemma":[0.9986061,0.0001405041,0.0000502924,0.0002456634,0.0005055651,0.000451916],"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":[0.0001860788,0.0003970341,0.02105573,0.001289218,0.00002211169,0.000002702958,0.1228391,2.856154e-7,0.00005706144,0.0000516338,0.01190246,0.8421966],"study_design_scores_gemma":[0.00120534,0.008508851,0.1840236,0.01116804,0.000536593,0.00001935782,0.5565861,0.00107476,0.02684342,0.0004608943,0.2079947,0.001578406],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863986,0.0005333581,0.0002494003,0.005991545,0.003481395,0.002419405,0.0000242354,0.0001168192,0.0007851776],"genre_scores_gemma":[0.9914152,0.00001723172,0.0004195627,0.002528606,0.00253947,0.001759519,0.0005311114,0.00003072641,0.0007585487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8406182,"threshold_uncertainty_score":0.4895212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0306914500917248,"score_gpt":0.4389907639000641,"score_spread":0.4082993138083393,"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."}}