{"id":"W4400174108","doi":"10.2196/55204","title":"Readability of Information Generated by ChatGPT for Hidradenitis Suppurativa","year":2024,"lang":"en","type":"letter","venue":"JMIR Dermatology","topic":"Hidradenitis Suppurativa and Treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hidradenitis suppurativa; Readability; Medicine; Computer science; Information retrieval; Dermatology; Pathology","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":[0.003688093,0.0002822568,0.0004698983,0.0008938169,0.00224152,0.002489699,0.0006883653,0.01012919,0.03149966],"category_scores_gemma":[0.05929709,0.0001861471,0.0006463532,0.0005070519,0.00078489,0.001284096,0.00113922,0.005896547,0.006771531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003627261,"about_ca_system_score_gemma":0.001205187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007982189,"about_ca_topic_score_gemma":0.006615597,"domain_scores_codex":[0.9941507,0.002973709,0.0006762861,0.0002807605,0.001189885,0.00072875],"domain_scores_gemma":[0.9475285,0.03931058,0.003021721,0.001108608,0.006484951,0.002545645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0006144136,0.0002670724,0.01832898,0.0002780879,0.00002927131,0.03748655,0.005429609,0.0002031745,0.002037315,0.002639145,0.8812618,0.05142457],"study_design_scores_gemma":[0.0004018178,0.001178481,0.05249876,0.002187804,0.0001391185,0.05638466,0.01088274,0.003632413,0.003535158,0.005863434,0.8631132,0.0001822718],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0390329,0.002121104,0.0004179094,0.8694499,0.009707449,0.00007057475,0.000447428,0.0002054192,0.07854735],"genre_scores_gemma":[0.3368544,0.001500457,0.0006448074,0.5872532,0.02503039,0.00008622452,0.0003120027,0.0002717801,0.04804667],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03149966,"threshold_uncertainty_score":0.1053768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380312685588859,"score_gpt":0.2886362493235503,"score_spread":0.2748331224676617,"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."}}