{"id":"W2536039441","doi":"10.3233/978-1-61499-678-1-322","title":"Transcription of Case Report Forms from Unstructured Referral Letters: A Semantic Text Analytics Approach","year":2016,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Referral; SNOMED CT; Ontology; Computer science; Natural language processing; Analytics; Information retrieval; Artificial intelligence; Data science; Medicine; Family medicine; Terminology; Linguistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003261189,0.0001293951,0.0003346851,0.0001876366,0.00007996183,0.000002630611,0.0001016956,0.0002669194,9.745605e-7],"category_scores_gemma":[0.0003240155,0.00008045986,0.00003280498,0.0002016112,0.0008638884,0.000008091778,0.0001013407,0.0001281828,3.537734e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002441451,"about_ca_system_score_gemma":0.00004023826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002454338,"about_ca_topic_score_gemma":0.00006330646,"domain_scores_codex":[0.9987874,0.00002302625,0.0007230296,0.0001464817,0.00007782492,0.0002422333],"domain_scores_gemma":[0.9993266,0.00002908243,0.0002830508,0.000278254,0.00004793414,0.00003506024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007916403,0.0003907247,0.09571047,0.005870169,0.001836781,0.001368067,0.01639983,0.00004870639,0.03048516,0.008202111,0.008328206,0.8305681],"study_design_scores_gemma":[0.03798092,0.01842951,0.04015949,0.007323889,0.00080609,0.1066914,0.4250465,0.005962337,0.1054483,0.08265928,0.1635015,0.005990778],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782439,0.003334809,0.01612779,0.001864924,0.000130431,0.0001593952,0.0000252545,0.00003007781,0.00008336529],"genre_scores_gemma":[0.9807609,0.002188921,0.01671726,0.0002489232,0.00002207413,0.00001514999,0.0000198872,0.000005407955,0.00002150111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8245773,"threshold_uncertainty_score":0.3281058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04595609939916981,"score_gpt":0.3306452819070362,"score_spread":0.2846891825078665,"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."}}