{"id":"W4406055608","doi":"10.2196/68139","title":"Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences","keywords":"Preprint; Informed consent; Best practice; Medical education; Computer science; Medicine; Psychology; Alternative medicine; World Wide Web; Political science; Law","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1104689,0.001729319,0.001385747,0.001610725,0.001370737,0.002562658,0.002232921,0.001513216,0.008145247],"category_scores_gemma":[0.1925692,0.001185418,0.003377154,0.001636448,0.001163113,0.001997034,0.00248525,0.00270613,0.001113654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002644334,"about_ca_system_score_gemma":0.004671699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002126492,"about_ca_topic_score_gemma":0.003196601,"domain_scores_codex":[0.8764724,0.1085695,0.004903706,0.004684038,0.004257708,0.001112623],"domain_scores_gemma":[0.6491065,0.3106259,0.01379635,0.01665589,0.008945584,0.0008696568],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.03445577,0.03774292,0.1641706,0.01582952,0.008930121,0.002045223,0.06672257,0.02437053,0.005556298,0.02772594,0.01025507,0.6021954],"study_design_scores_gemma":[0.03245904,0.1153324,0.1684254,0.01269888,0.01670398,0.002474011,0.05259749,0.4139556,0.0304815,0.07719722,0.07573854,0.001935919],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7089436,0.00159717,0.230039,0.0007759887,0.0001741635,0.05181277,0.001970756,0.0004443814,0.004242097],"genre_scores_gemma":[0.6656723,0.0004622862,0.2389285,0.0009802348,0.0001013646,0.09127089,0.0009823911,0.0002107897,0.001391276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8895311,"threshold_uncertainty_score":0.5842223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4476063200192653,"score_gpt":0.6371730859460205,"score_spread":0.1895667659267552,"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."}}