{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005757768,0.0008845171,0.0005292562,0.006105228,0.001581499,0.005017797,0.001072942,0.001107904,0.007448157],"category_scores_gemma":[0.01939394,0.0007529886,0.002310483,0.006699705,0.00101514,0.007991638,0.004139467,0.001255328,0.00167929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001574542,"about_ca_system_score_gemma":0.00226621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006419541,"about_ca_topic_score_gemma":0.009659842,"domain_scores_codex":[0.9979846,0.0007887727,0.0002762825,0.0003899435,0.0004475523,0.000112809],"domain_scores_gemma":[0.9907084,0.006926225,0.0004681169,0.001018662,0.0006348795,0.0002437145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008076705,0.0002781143,0.02175127,0.002203907,0.0004291031,0.001700717,0.02143249,0.02313301,0.01041167,0.2431461,0.0312269,0.643479],"study_design_scores_gemma":[0.0001650158,0.0003497761,0.01375654,0.0009790572,0.0004326385,0.001642373,0.01025327,0.3330861,0.01685103,0.3837649,0.2384583,0.0002610016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0162549,0.0002809856,0.9633649,0.0004421228,0.0000559642,0.0004927027,0.005357751,0.00975166,0.003999067],"genre_scores_gemma":[0.1010856,0.0002780689,0.8866003,0.0000641288,0.00003473715,0.0007206871,0.008433418,0.000940375,0.001842679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007448157,"threshold_uncertainty_score":0.03045034,"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."}}