{"id":"W4414015785","doi":"10.11159/icbes25.120","title":"ACUITEE: A Comprehensive Tool for Visualization, Editing and Curating textual Annotations in Clinical Data","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Visualization; Data visualization; Information retrieval; Information visualization; World Wide Web; Human–computer interaction; Natural language processing; 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":[0.007492365,0.002673496,0.001219488,0.008132225,0.001403136,0.004746734,0.003089063,0.002057035,0.04769832],"category_scores_gemma":[0.03001755,0.001342223,0.001818194,0.005218509,0.0008391149,0.004799545,0.007004041,0.002306463,0.02225943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293563,"about_ca_system_score_gemma":0.00388556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004603686,"about_ca_topic_score_gemma":0.009004837,"domain_scores_codex":[0.9953501,0.001489545,0.0007643044,0.0007556893,0.001457151,0.000183149],"domain_scores_gemma":[0.9785396,0.01433,0.001214907,0.002577737,0.002545353,0.0007924499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007722207,0.0001431204,0.002225036,0.004572855,0.0003293756,0.001196323,0.002613892,0.001882839,0.01679281,0.009922445,0.6186182,0.3409308],"study_design_scores_gemma":[0.0003046207,0.00013322,0.004905164,0.001267226,0.0001446266,0.002196156,0.0005898582,0.02649244,0.02299879,0.02004082,0.9205732,0.0003538859],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002956401,0.001568144,0.5123538,0.001554069,0.0004464726,0.001238798,0.05772759,0.4119913,0.0101634],"genre_scores_gemma":[0.02678122,0.001692461,0.8222796,0.00196194,0.0003221459,0.003206146,0.08796948,0.04313596,0.01265101],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.04769832,"threshold_uncertainty_score":0.1595668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180454020220171,"score_gpt":0.3175173256400033,"score_spread":0.2957127854378016,"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."}}