{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.03170709,0.0001955861,0.0004383451,0.000444074,0.00443234,0.0001205751,0.0003575657,0.0001497978,0.0007743639],"category_scores_gemma":[0.0005777696,0.000163432,0.00003625733,0.0008894324,0.0001335365,0.001793796,0.001368865,0.002134288,0.0001733501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001867163,"about_ca_system_score_gemma":0.001643341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004628435,"about_ca_topic_score_gemma":0.000008709245,"domain_scores_codex":[0.9860317,0.008597052,0.001893246,0.0005017091,0.001841511,0.001134774],"domain_scores_gemma":[0.9953008,0.001943794,0.000435777,0.000519426,0.0006763317,0.001123824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002601928,0.0008576221,0.06514939,0.01778384,0.00006078793,0.000007651963,0.4245488,0.0000447396,0.00004756103,0.005558136,0.0548588,0.4284807],"study_design_scores_gemma":[0.0108819,0.009866364,0.1576904,0.002046096,0.00001163274,0.00006204795,0.1414205,0.1539475,0.0001220932,0.003379222,0.5194009,0.001171362],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594234,0.0002469023,0.01769226,0.004258112,0.0005481557,0.015175,0.0003170418,0.0002124811,0.002126643],"genre_scores_gemma":[0.9869834,0.00005571604,0.001175976,0.002620816,0.00008216077,0.008612672,0.0001137266,0.00001921157,0.0003363733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4645421,"threshold_uncertainty_score":0.9970613,"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."}}