{"id":"W3013597571","doi":"10.2196/16670","title":"Patient Perception of Plain-Language Medical Notes Generated Using Artificial Intelligence Software: Pilot Mixed-Methods Study","year":2020,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Comprehension; Plain language; Documentation; Medical education; Health care; Psychology; Unified Medical Language System; Patient satisfaction; Medicine; Artificial intelligence; Computer science; Nursing","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":[0.01396412,0.0007190203,0.0009750518,0.0008115178,0.001513203,0.001822023,0.0008313888,0.001114805,0.003876381],"category_scores_gemma":[0.02465895,0.0007156105,0.001042074,0.000562596,0.00115136,0.00175146,0.001292182,0.001367449,0.0007077128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278261,"about_ca_system_score_gemma":0.002158219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001183788,"about_ca_topic_score_gemma":0.002073837,"domain_scores_codex":[0.99328,0.004363275,0.0005956443,0.000590936,0.00066003,0.0005101907],"domain_scores_gemma":[0.9766424,0.0149524,0.003073905,0.001075222,0.002872442,0.001383667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0196976,0.09524769,0.4918807,0.002028884,0.0006466644,0.002320522,0.2830943,0.0007162624,0.007801494,0.0006106204,0.002739462,0.09321582],"study_design_scores_gemma":[0.007542685,0.2621652,0.5063232,0.0006101335,0.0007235568,0.001915744,0.2021789,0.005224861,0.005118849,0.001011327,0.006722305,0.0004632226],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986814,0.0000299863,0.0002650832,0.00005310768,0.000004956309,0.0006689241,0.00007098154,0.000005298942,0.0002202991],"genre_scores_gemma":[0.9939802,0.0001355703,0.002069952,0.0003266871,0.00002305742,0.00285327,0.0001168681,0.00000682305,0.0004875419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01396412,"threshold_uncertainty_score":0.07385021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2930984704009103,"score_gpt":0.6006857799274068,"score_spread":0.3075873095264965,"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."}}