{"id":"W3094834348","doi":"10.2196/23375","title":"The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; U.S. National Library of Medicine","keywords":"Computer science; Automatic summarization; Natural language processing; Information retrieval; Semantic similarity; Artificial intelligence; Task (project management); Unified Medical Language System; Semantics (computer science); Sentence; Similarity (geometry); Set (abstract data type); Semantic computing; Semantic 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":[0.04202833,0.003596036,0.00351724,0.01324312,0.005255091,0.009386616,0.009724856,0.006015382,0.03250461],"category_scores_gemma":[0.07483736,0.001808949,0.003518112,0.01097281,0.002494596,0.01642047,0.01860503,0.009110782,0.03593402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005933921,"about_ca_system_score_gemma":0.01968425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03949036,"about_ca_topic_score_gemma":0.06630088,"domain_scores_codex":[0.9697368,0.008103461,0.002595282,0.005138462,0.01221613,0.002210008],"domain_scores_gemma":[0.8889642,0.02968404,0.003821551,0.01799453,0.04392749,0.01560814],"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":[0.0003331296,0.0003380758,0.002432174,0.0009199041,0.000107734,0.0001501985,0.0002837782,0.001082978,0.002690976,0.001583297,0.9386088,0.05146892],"study_design_scores_gemma":[0.000497443,0.0004507262,0.01015256,0.0005985545,0.0001213257,0.0003829302,0.000786818,0.01385841,0.005108189,0.007181377,0.9606749,0.0001867093],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.02911266,0.01107509,0.1228769,0.06651846,0.01504444,0.007463053,0.6505544,0.05209559,0.0452593],"genre_scores_gemma":[0.009165428,0.001528108,0.06243294,0.005842403,0.001231439,0.003549458,0.8999338,0.003382262,0.01293414],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04202833,"threshold_uncertainty_score":0.2222697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09220643652773049,"score_gpt":0.3689164150421732,"score_spread":0.2767099785144427,"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."}}