{"id":"W4224288542","doi":"10.2196/35069","title":"A Smart Mobile App to Simplify Medical Documents and Improve Health Literacy: System Design and Feasibility Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Information and Intelligent Systems; National Science Foundation","keywords":"Health literacy; Computer science; Reading (process); Literacy; Control (management); Health care; Medical education; Human–computer interaction; Psychology; Medicine; Artificial intelligence","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":[0.003675939,0.0006284422,0.0005042393,0.0007226163,0.0004054512,0.00077341,0.0008771769,0.0009604905,0.008047134],"category_scores_gemma":[0.00761737,0.0004190129,0.0005111073,0.0002539793,0.0005092184,0.0009816408,0.00103039,0.0003860375,0.001508819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004322413,"about_ca_system_score_gemma":0.001199062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000870895,"about_ca_topic_score_gemma":0.001189656,"domain_scores_codex":[0.9988827,0.0004771505,0.0001481714,0.0002057874,0.0002052242,0.00008104865],"domain_scores_gemma":[0.9953805,0.002945936,0.0001750234,0.0002657242,0.001037359,0.0001955167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01313112,0.01563019,0.05362174,0.01021297,0.0003605361,0.001667097,0.008667866,0.003722379,0.1076087,0.0020255,0.01329398,0.770058],"study_design_scores_gemma":[0.01973623,0.1633978,0.3434359,0.003563816,0.003068747,0.00605173,0.008523725,0.1054145,0.2233106,0.003510986,0.1190398,0.0009461349],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8729757,0.0006430044,0.07407022,0.0006169634,0.0001947022,0.04037645,0.002967387,0.003806472,0.004349007],"genre_scores_gemma":[0.5961486,0.0007486625,0.3506646,0.000668832,0.00007901428,0.04248076,0.00209242,0.0002089177,0.006908192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008047134,"threshold_uncertainty_score":0.02692032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1123133811241981,"score_gpt":0.5566690419546323,"score_spread":0.4443556608304342,"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."}}