{"id":"W2160476655","doi":"10.1177/1460458208096556","title":"Topic maps for exploring nosological, lexical, semantic and HL7 structures for clinical data","year":2008,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Dalhousie University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Information retrieval; Semantic interoperability; Semantic integration; Referent; Terminology; Systematized Nomenclature of Medicine; Natural language processing; SNOMED CT; Interoperability; Linguistics; Semantic Web; World Wide Web; Semantic computing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001076475,0.0001234396,0.0002827816,0.0000364143,0.0004837992,0.00004040275,0.0003211873,0.0001763146,0.000003666451],"category_scores_gemma":[0.001082821,0.00008975345,0.00007437704,0.00003168323,0.0002251234,0.00001555024,0.0001766786,0.0002282166,9.83777e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009160699,"about_ca_system_score_gemma":0.000233326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001728146,"about_ca_topic_score_gemma":0.000002952204,"domain_scores_codex":[0.9984307,0.00004582908,0.0008854971,0.0001482798,0.0001231377,0.0003665856],"domain_scores_gemma":[0.9988977,0.0001454278,0.0003179703,0.0002999249,0.000079408,0.0002596233],"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":[0.0004861729,0.0001492718,0.0111644,0.00100679,0.0002025354,0.00000762437,0.001180094,0.00001697089,0.0001101553,0.0005153851,0.3739712,0.6111894],"study_design_scores_gemma":[0.002214024,0.002543238,0.006795235,0.00005865322,0.0000215181,0.0008910852,0.0007734919,0.00218757,0.0001308389,0.001497869,0.9826493,0.0002372007],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7676095,0.00447659,0.221967,0.003726076,0.001344286,0.0005885569,0.0001885037,0.00003320211,0.0000662556],"genre_scores_gemma":[0.3205658,0.04912675,0.6169199,0.009278608,0.003193214,0.00007364397,0.0006191721,0.00003653202,0.0001863325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6109522,"threshold_uncertainty_score":0.3721042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4838173379846424,"score_gpt":0.4623620342121831,"score_spread":0.0214553037724593,"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."}}