{"id":"W4411839987","doi":"10.2196/73642","title":"Using Artificial Intelligence ChatGPT to Access Medical Information About Chemical Eye Injuries: Comparative Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Readability; Thematic analysis; Terminology; Thematic map; Medicine; Computer science; Optometry; Qualitative research","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":[0.01083533,0.000407303,0.0006717213,0.003232889,0.001030681,0.001513737,0.0007411785,0.0007700858,0.003548922],"category_scores_gemma":[0.05584282,0.0002909706,0.0006947069,0.002151893,0.001047587,0.002260022,0.002286363,0.001060648,0.0006946609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001580231,"about_ca_system_score_gemma":0.001567242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004155989,"about_ca_topic_score_gemma":0.005348685,"domain_scores_codex":[0.9902951,0.006808622,0.0008531418,0.0005198159,0.001015377,0.0005080513],"domain_scores_gemma":[0.9390609,0.04653085,0.003796172,0.002026702,0.006234288,0.002350997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002707038,0.008077713,0.516894,0.002948362,0.0002758458,0.002533453,0.2635548,0.0005404929,0.002156338,0.0007159003,0.002583225,0.1970129],"study_design_scores_gemma":[0.0004534305,0.01458257,0.7491469,0.0009616048,0.0005163697,0.003278736,0.209335,0.005336202,0.00252404,0.0006437649,0.01303413,0.000187358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967262,0.0001679187,0.0006091176,0.0001050928,0.00001608292,0.0003370218,0.000124489,0.00002037571,0.001893793],"genre_scores_gemma":[0.9951001,0.0004943623,0.002196619,0.0001726543,0.0000356245,0.0007182048,0.0002721709,0.00001546951,0.0009947498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01083533,"threshold_uncertainty_score":0.05730343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4368999134173692,"score_gpt":0.6395663771934329,"score_spread":0.2026664637760637,"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."}}