{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08890187,0.002365891,0.005861966,0.03940239,0.002202402,0.008974019,0.004101505,0.004965403,0.003778129],"category_scores_gemma":[0.3317867,0.001797161,0.006914848,0.03563578,0.003294933,0.009386566,0.004921798,0.003054848,0.0007960712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008005046,"about_ca_system_score_gemma":0.03486011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005440771,"about_ca_topic_score_gemma":0.008527502,"domain_scores_codex":[0.907286,0.04493636,0.03257741,0.003151935,0.01116712,0.0008811211],"domain_scores_gemma":[0.5351257,0.3872349,0.03655898,0.009280383,0.03056462,0.001235433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001853181,0.00007729076,0.001365556,0.6885052,0.002438635,0.0001999797,0.002524065,0.0007173491,0.0003181327,0.002827036,0.006085732,0.2947558],"study_design_scores_gemma":[0.00005319086,0.0001500155,0.001386249,0.9516615,0.006384459,0.0002832263,0.001483074,0.0003739966,0.0004800422,0.002011306,0.03568149,0.00005136345],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00179081,0.9849819,0.0043655,0.003230706,0.0006399201,0.002330189,0.0005681242,0.00006425691,0.002028593],"genre_scores_gemma":[0.02369743,0.9541998,0.01482835,0.001498137,0.0004792376,0.004271278,0.0007690104,0.00003772167,0.0002190012],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.08890187,"threshold_uncertainty_score":0.4701636,"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."}}