{"id":"W3117079728","doi":"10.2196/31980","title":"Expressiveness of an International Semantic Standard for Wound Care: Mapping a Standardized Item Set for Leg Ulcers to the Systematized Nomenclature of Medicine–Clinical Terms","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung","keywords":"SNOMED CT; Terminology; Systematized Nomenclature of Medicine; Interoperability; Medicine; Health care; Documentation; Artificial intelligence; Computer science; World Wide Web","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":[0.03929461,0.000583358,0.0006154297,0.00680877,0.001074744,0.003119626,0.001129866,0.0009922453,0.00195557],"category_scores_gemma":[0.1309338,0.0002722761,0.001187788,0.005932624,0.002005823,0.002643526,0.003806875,0.001136299,0.0005152777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002958999,"about_ca_system_score_gemma":0.006339384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002940881,"about_ca_topic_score_gemma":0.003190023,"domain_scores_codex":[0.9590054,0.02366643,0.007442919,0.001803631,0.007603968,0.0004777077],"domain_scores_gemma":[0.9040935,0.06053084,0.006640206,0.01250861,0.0154669,0.0007600298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001382264,0.0008738305,0.1650506,0.007818354,0.0005312686,0.001122595,0.08426721,0.00904199,0.03336988,0.08947271,0.02339086,0.5836785],"study_design_scores_gemma":[0.000498767,0.00247841,0.4679486,0.01359821,0.001494153,0.004857391,0.07304195,0.05312807,0.0387198,0.1161207,0.2274213,0.0006927752],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.608106,0.001477577,0.3372504,0.002513409,0.0004596416,0.006995625,0.009718986,0.0006577249,0.03282062],"genre_scores_gemma":[0.6948224,0.000471909,0.2873209,0.0003031815,0.00003892767,0.005540652,0.0104552,0.0001423717,0.0009045807],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03929461,"threshold_uncertainty_score":0.2078122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03386969212034033,"score_gpt":0.3805468065372176,"score_spread":0.3466771144168773,"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."}}