{"id":"W4389083881","doi":"10.2196/44639","title":"Patient Information Summarization in Clinical Settings: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Computer science; MEDLINE; Medicine; Data science; Information retrieval","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":[],"consensus_categories":[],"category_scores_codex":[0.001242915,0.0001122321,0.0002302104,0.00009177912,0.0000414293,0.00002060142,0.0001847642,0.000338997,0.00004349187],"category_scores_gemma":[0.002487139,0.00009226048,0.00007271828,0.0003942588,0.0001261559,0.00002047303,0.0002096547,0.0002371699,0.0001858503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001152456,"about_ca_system_score_gemma":0.0002230365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002019293,"about_ca_topic_score_gemma":0.000007531294,"domain_scores_codex":[0.997875,0.00007657046,0.001232481,0.00008878978,0.0004642959,0.0002629041],"domain_scores_gemma":[0.999244,0.00007036527,0.000238932,0.0002211968,0.0000650619,0.0001604026],"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":[0.0000273163,0.00006835716,0.003325434,0.005618894,0.00002145295,0.000006901303,0.0009326661,0.00001112482,0.00001489565,0.00005601219,0.124296,0.865621],"study_design_scores_gemma":[0.005705964,0.002323771,0.01698553,0.1247406,0.00005907334,0.0001048548,0.00695227,0.02518735,0.001065026,0.0002609259,0.8148432,0.001771427],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544234,0.005994088,0.009666665,0.009348231,0.002193174,0.00415958,0.00005493493,0.0006918867,0.013468],"genre_scores_gemma":[0.558057,0.1950068,0.02183421,0.204972,0.002082414,0.002101985,0.01522165,0.0001429632,0.0005809838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8638496,"threshold_uncertainty_score":0.3762273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331236175518733,"score_gpt":0.3673106231275995,"score_spread":0.3439982613724122,"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."}}