{"id":"W4281493375","doi":"10.3233/shti220403","title":"Pretrained Neural Networks Accurately Identify Cancer Recurrence in Medical Record","year":2022,"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":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Waterloo","funders":"","keywords":"Medical record; Computer science; Task (project management); Cancer; Artificial intelligence; Disease; Colorectal cancer; Domain (mathematical analysis); Breast cancer; Natural language processing; Medicine; Machine learning; Surgery; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001070528,0.000803255,0.0004775859,0.0007368915,0.0003309333,0.0007613907,0.0007655162,0.000955371,0.00152947],"category_scores_gemma":[0.005653032,0.0002991023,0.0005354683,0.0007809805,0.000226462,0.001294114,0.0004851373,0.001118992,0.0008197497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040688,"about_ca_system_score_gemma":0.001038927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.020769,"about_ca_topic_score_gemma":0.02800461,"domain_scores_codex":[0.9995826,0.00007311036,0.00004086043,0.0001782302,0.0000590018,0.00006620409],"domain_scores_gemma":[0.9978561,0.001383186,0.0002123526,0.0001488992,0.0003468037,0.00005273347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006480187,0.0007717048,0.05946077,0.0002505223,0.0003282618,0.0004245816,0.0001880635,0.4525428,0.005384392,0.00140729,0.01833672,0.4602568],"study_design_scores_gemma":[0.00001069941,0.00006241225,0.004681599,0.0000234759,0.00003701381,0.00003754087,0.00003589024,0.9922334,0.001041607,0.001053436,0.000774385,0.000008634143],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8187028,0.004169132,0.1560821,0.002424557,0.0005450992,0.0001758091,0.005422406,0.004250923,0.008227115],"genre_scores_gemma":[0.961623,0.0005667364,0.02809138,0.0003890611,0.0001251373,0.00008233522,0.005899314,0.00005851413,0.003164498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.020769,"threshold_uncertainty_score":0.04129624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06847369989992134,"score_gpt":0.4168528629910206,"score_spread":0.3483791630910993,"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."}}