{"id":"W4392524291","doi":"10.2196/55898","title":"Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study","year":2024,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Reading (process); Computer science; Linguistics; World Wide Web; Philosophy","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":[0.04122072,0.0004736829,0.0005722367,0.001539932,0.004113727,0.003780119,0.001861995,0.001100817,0.002306534],"category_scores_gemma":[0.07920951,0.0006258135,0.0005040133,0.0009186596,0.006152785,0.003827388,0.005717668,0.00216088,0.0002963332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005354972,"about_ca_system_score_gemma":0.00607612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004556518,"about_ca_topic_score_gemma":0.006220175,"domain_scores_codex":[0.9702975,0.02455968,0.0007694787,0.001144553,0.001936247,0.001292505],"domain_scores_gemma":[0.9046609,0.08145132,0.004132106,0.001614767,0.005272252,0.00286865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002709518,0.0001142065,0.006397468,0.0001919127,0.000007399552,0.0002558005,0.985008,0.00005660914,0.0007508434,0.0005102897,0.0002592377,0.006421196],"study_design_scores_gemma":[0.00001267142,0.0001355838,0.003404769,0.0001971868,0.00001121436,0.0002146945,0.9906574,0.0003927261,0.0005771664,0.0003339445,0.004037098,0.00002536887],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933612,0.0001428368,0.002241579,0.0009864408,0.00001727032,0.0003115822,0.00007069787,0.00001860987,0.002849744],"genre_scores_gemma":[0.995577,0.0001869454,0.002424955,0.0003882128,0.00000554598,0.000381278,0.00004049548,0.00002435274,0.0009711209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04122072,"threshold_uncertainty_score":0.2179986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1105346221337001,"score_gpt":0.5642744455566059,"score_spread":0.4537398234229058,"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."}}